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diff --git a/_sources/addition_on_ibmq.ipynb b/_sources/addition_on_ibmq.ipynb
index d0bc4d5..f61b946 100644
--- a/_sources/addition_on_ibmq.ipynb
+++ b/_sources/addition_on_ibmq.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "64b1832f",
+   "id": "003360c0",
    "metadata": {},
    "source": [
     "# 【参考】足し算を実機で行う\n",
@@ -19,7 +19,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e9c7929d",
+   "id": "c1c028f6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "60fd72c6",
+   "id": "71149203",
    "metadata": {},
    "source": [
     "実習と同様、`'ibm-q/open/main'`のプロバイダでは(1, 1)の回路しか扱えないので、フェイクバックエンドを使います。"
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "21998db8",
+   "id": "c1169c5c",
    "metadata": {
     "tags": [
      "remove-output",
@@ -107,7 +107,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "40208724",
+   "id": "71f3d4cd",
    "metadata": {
     "tags": [
      "remove-output"
@@ -125,7 +125,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_12/3657533314.py:2: DeprecationWarning: The module 'qiskit.test.mock' is deprecated since Qiskit Terra 0.21.0, and will be removed 3 months or more later. Instead, you should import the desired object directly 'qiskit.providers.fake_provider'.\n",
+      "/tmp/ipykernel_11/3657533314.py:2: DeprecationWarning: The module 'qiskit.test.mock' is deprecated since Qiskit Terra 0.21.0, and will be removed 3 months or more later. Instead, you should import the desired object directly 'qiskit.providers.fake_provider'.\n",
       "  from qiskit.test.mock import FakeGuadalupe\n"
      ]
     }
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9af9585",
+   "id": "d211d491",
    "metadata": {},
    "source": [
     "実習と全く同じ`setup_addition`関数と、次のセルで効率化前の回路を返す`make_original_circuit`関数を定義します。"
@@ -154,7 +154,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "42727346",
+   "id": "acbf9a4b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6b663c0c",
+   "id": "72771fe7",
    "metadata": {},
    "source": [
     "(4, 4)から(1, 1)までそれぞれオリジナルと効率化した回路の二通りを作り、全てリストにまとめてバックエンドに送ります。"
@@ -225,7 +225,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "353af8e6",
+   "id": "d770f1e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f909ca05",
+   "id": "01b8c94f",
    "metadata": {
     "tags": [
      "remove-input"
@@ -287,7 +287,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9874f0aa",
+   "id": "6cbc57da",
    "metadata": {
     "tags": [
      "remove-output",
@@ -339,7 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "de755ba7",
+   "id": "058180bf",
    "metadata": {
     "tags": [
      "remove-input"
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0559261b",
+   "id": "477afa37",
    "metadata": {},
    "source": [
     "ジョブが返ってきたら、正しい足し算を表しているものの割合を調べてみましょう。"
@@ -366,7 +366,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "ee91c974",
+   "id": "09d1b4d5",
    "metadata": {
     "tags": [
      "remove-output"
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04737a2d",
+   "id": "14967d6a",
    "metadata": {},
    "source": [
     "ちなみに、`ibm_kawasaki`というマシンで同じコードを走らせると、下のような結果が得られます。\n",
@@ -452,7 +452,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e8ea8958",
+   "id": "50cc2a7a",
    "metadata": {
     "tags": [
      "remove-input"
@@ -473,7 +473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "44aeb1fb",
+   "id": "78142a37",
    "metadata": {},
    "source": [
     "回路が均一にランダムに$0$から$2^{n_1 + n_2 + n_3} - 1$までの数を返す場合、レジスタ1と2のそれぞれの値の組み合わせに対して正しいレジスタ3の値が一つあるので、正答率は$2^{n_1 + n_2} / 2^{n_1 + n_2 + n_3} = 2^{-n_3}$となります。実機では、(4, 4)と(3, 3)でどちらの回路も正答率がほとんどこの値に近くなっています。(2, 2)では効率化回路で明らかにランダムでない結果が出ています。(1, 1)では両回路とも正答率8割です。\n",
@@ -483,7 +483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eb8572ee",
+   "id": "211acb82",
    "metadata": {},
    "source": [
     "## Quantum Volume\n",
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "9eaca214",
+   "id": "16547dbf",
    "metadata": {
     "tags": [
      "remove-output",
@@ -533,7 +533,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "4368db8b",
+   "id": "44e6d0c9",
    "metadata": {
     "tags": [
      "remove-output",
@@ -579,7 +579,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "644172bc",
+   "id": "97d7271d",
    "metadata": {},
    "source": [
     "QVから期待される正答率の序列にならないかもしれません。量子コンピュータという恐ろしく複雑な機械の性能を一つの数値で表すことの限界がここにあり、今のように単純な回路を実行する場合は、ケースバイケースで特定の量子ビットや特定のゲートのエラー率が結果に大きな影響を及ぼしたりするのです。\n",
@@ -590,7 +590,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "5dca838f",
+   "id": "79a9e264",
    "metadata": {
     "tags": [
      "remove-output",
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b86149db",
+   "id": "01ff7e6c",
    "metadata": {
     "tags": [
      "remove-output",
@@ -657,7 +657,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "25c4459c",
+   "id": "bc6df371",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -693,7 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "a4053852",
+   "id": "5b5449c9",
    "metadata": {
     "tags": [
      "remove-output",
diff --git a/_sources/chsh_inequality.ipynb b/_sources/chsh_inequality.ipynb
index 2cbd07f..15254ff 100644
--- a/_sources/chsh_inequality.ipynb
+++ b/_sources/chsh_inequality.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "4719771b",
+   "id": "6e3117eb",
    "metadata": {},
    "source": [
     "# CHSH不等式の破れを確認する"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9f2d0d7",
+   "id": "d9d7ea8d",
    "metadata": {},
    "source": [
     "この最初の実習では、量子コンピュータにおいて量子力学的状態、特に「**エンタングルメント**」が実現しているか検証してみましょう。実習を通じて量子力学の概念と量子コンピューティングの基礎を紹介していきます。\n",
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3112de3c",
+   "id": "1a6b3d48",
    "metadata": {},
    "source": [
     "## 本当に量子コンピュータなのか？\n",
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bd6f3dc",
+   "id": "60a2b9e8",
    "metadata": {},
    "source": [
     "## CHSH不等式\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7fe2fb8a",
+   "id": "607825aa",
    "metadata": {},
    "source": [
     "## Qiskitの基本構造\n",
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5ab9f677",
+   "id": "a5e14e7c",
    "metadata": {},
    "source": [
     "### 量子ビット、量子レジスタ\n",
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fe7864a",
+   "id": "2d5e1434",
    "metadata": {},
    "source": [
     "### ゲート、回路、測定\n",
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ea49f791",
+   "id": "b5511262",
    "metadata": {},
    "source": [
     "### 量子計算結果の解析\n",
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "40e933db",
+   "id": "d8fa3126",
    "metadata": {},
    "source": [
     "(common_gates)=\n",
@@ -264,7 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e2ebb97b",
+   "id": "46b1c2c2",
    "metadata": {
     "tags": [
      "remove-output"
@@ -298,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "56648404",
+   "id": "3d9970ad",
    "metadata": {},
    "outputs": [
     {
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99b2a55d",
+   "id": "f13377d4",
    "metadata": {},
    "source": [
     "最後のプリント文で、ゲートが3つなのにも関わらず \"5 operations\" と出てくるのは、各量子ビットの測定も一つのオペレーションとして数えられるからです。\n",
@@ -385,7 +385,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c8bed752",
+   "id": "98d44fce",
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0b9ea0b",
+   "id": "d2d30c32",
    "metadata": {},
    "source": [
     "やや複雑ですが、また計算を追ってみましょう。まず角度$\\theta_1, \\theta_2, \\theta_3$は定義から以下の関係を満たします。\n",
@@ -445,7 +445,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "806501e2",
+   "id": "c9b13aef",
    "metadata": {},
    "source": [
     "### 回路図の描き方と読み方\n",
@@ -456,7 +456,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f63f86f8",
+   "id": "146257aa",
    "metadata": {},
    "outputs": [
     {
@@ -477,7 +477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33a75c87",
+   "id": "f619b008",
    "metadata": {},
    "source": [
     "　ここで`draw()`の引数`'mpl'`はmatplotlibライブラリを使ってカラーで描くことを指定しています。実行環境によっては対応していないこともあるので、その場合は引数なしの`draw()`を使います。結果は`mpl`の場合に比べて見劣りしますが、内容は同じです。"
@@ -486,7 +486,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "19984f90",
+   "id": "92cda928",
    "metadata": {},
    "outputs": [
     {
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0229a21a",
+   "id": "0b6a6306",
    "metadata": {},
    "source": [
     "回路図は左から右に読んでいきます。水平の2本の実線が上からそれぞれ第0、第1量子ビットに対応し、その上にかぶさっている四角がゲート、最後にある矢印が下に伸びている箱が測定を表します。1ビットゲートから伸びている先端の丸い縦線は制御を表します。一番下の二重線は「古典レジスタ」（量子現象のない物理学を「古典物理学」と呼ぶので、量子でない通常のコンピュータにまつわる概念にはよく「古典 classical」という接頭辞をつけます）に対応し、測定結果の0/1が記録される部分です。"
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d1104e5",
+   "id": "be6d64c2",
    "metadata": {},
    "source": [
     "## CHSH不等式を計算する回路を書く\n",
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "d0cd2ba2",
+   "id": "c522a7e6",
    "metadata": {},
    "outputs": [
     {
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "280171a8",
+   "id": "b6f68e5a",
    "metadata": {},
    "source": [
     "それぞれの回路で2ビットレジスタの基底$\\ket{00}, \\ket{01}, \\ket{10}, \\ket{11}$が現れる確率を計算してみましょう。\n",
@@ -705,7 +705,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "485fd15a",
+   "id": "7d8f9390",
    "metadata": {},
    "source": [
     "## 回路を実機で実行する\n",
@@ -716,7 +716,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "2644138d",
+   "id": "6beebae6",
    "metadata": {
     "tags": [
      "remove-output",
@@ -769,7 +769,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4891e0d8",
+   "id": "1d5decb2",
    "metadata": {},
    "source": [
     "認証が済んだら、利用する量子コンピュータ（「バックエンド」と呼びます）を選びます。"
@@ -778,7 +778,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "9f6bb0ea",
+   "id": "7c70a58d",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bad1f5b",
+   "id": "325f2d4d",
    "metadata": {},
    "source": [
     "回路をバックエンドに送るには、`transpile`という関数とバックエンドの`run`というメソッドを使います。`transpile`については次回{ref}`transpilation`で説明するので、今は「おまじない」だと思ってください。`run`で回路を送るとき、前述したように同時にショット数を指定します。バックエンドごとに一度のジョブでの最大ショット数が決められており、8192、30000、100000などとさまざまです。回路をバックエンドに渡し、`shots`回実行させることをジョブと呼びます。"
@@ -820,7 +820,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "f527106c",
+   "id": "600859f9",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -856,7 +856,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dacadf87",
+   "id": "d542e2ea",
    "metadata": {},
    "source": [
     "これで回路がバックエンドに送られ、キューに入りました。ジョブの実行結果は`run`メソッドの返り値であるジョブオブジェクトから参照します。\n",
@@ -872,7 +872,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "024b1df7",
+   "id": "a9cbac3c",
    "metadata": {},
    "source": [
     "## 量子測定結果の解析\n",
@@ -883,7 +883,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "8ed92dcd",
+   "id": "2b71e9e7",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -922,7 +922,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a452d36f",
+   "id": "6f182104",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -946,7 +946,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b206c74",
+   "id": "4dfb502e",
    "metadata": {},
    "source": [
     "````{tip}\n",
@@ -966,7 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "d37519d2",
+   "id": "feeca61c",
    "metadata": {},
    "outputs": [
     {
@@ -990,7 +990,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20f0e64e",
+   "id": "5f67cd63",
    "metadata": {},
    "source": [
     "$c^2/2 = (s + c)^2/4 = 0.427$, $s^2/2 = (s - c)^2 / 4 = 0.073$なので、得られた確率は当たらずとも遠からずというところでしょうか。\n",
@@ -1007,7 +1007,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "04a75b6c",
+   "id": "250d1af9",
    "metadata": {},
    "outputs": [
     {
@@ -1045,7 +1045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "977842fc",
+   "id": "39fa30e0",
    "metadata": {},
    "source": [
     "無事、$S$が2を超えました。"
diff --git a/_sources/circuit_from_scratch.ipynb b/_sources/circuit_from_scratch.ipynb
index 06add5b..1fc183c 100644
--- a/_sources/circuit_from_scratch.ipynb
+++ b/_sources/circuit_from_scratch.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "86eebb13",
+   "id": "376c0953",
    "metadata": {},
    "source": [
     "# 単純な量子回路をゼロから書く"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47820384",
+   "id": "84665a45",
    "metadata": {},
    "source": [
     "{doc}`chsh_inequality`で、量子回路の基本的な書き方と実機での実行の仕方を学びました。そこで出てきたように、QCでは量子レジスタに特定の量子状態を実現することが計算を行うこととなります。そこで今回は、基本的なゲートを組み合わせて様々な量子状態を作る実習を行います。\n",
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22791b75",
+   "id": "ad82d31b",
    "metadata": {},
    "source": [
     "## 状態ベクトルシミュレータで量子状態を調べる\n",
@@ -38,7 +38,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9cc83770",
+   "id": "176a2647",
    "metadata": {
     "tags": [
      "remove-output"
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e529516e",
+   "id": "71fd5e72",
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c46f832",
+   "id": "56bec084",
    "metadata": {},
    "source": [
     "シミュレートする回路を作ります。ここでは例として前半で登場した回路1を使います。測定以外のゲートをかけていきます。"
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1681cf2b",
+   "id": "57f53638",
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04b1ba71",
+   "id": "5ca199df",
    "metadata": {},
    "source": [
     "```{caution}\n",
@@ -135,7 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "086875d1",
+   "id": "e555267d",
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3de37ffa",
+   "id": "ea9acbb1",
    "metadata": {},
    "source": [
     "状態ベクトルは`np.asarray()`でnumpy配列に変換できます。データタイプは128ビットの複素数（実部と虚部それぞれ64ビット）です。配列のインデックスがそのまま二進数としてみなした計算基底の値に対応しています。\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "d75f62b1",
+   "id": "11bc7cfc",
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a1e3894a",
+   "id": "3b700b77",
    "metadata": {},
    "source": [
     "前回より、{eq}`回路1 <eqn-circuit1>`は$\\frac{1}{\\sqrt{2}} (s\\ket{00} + c\\ket{01} - c\\ket{10} + s\\ket{11})$（$s:=\\sin(\\pi/8)=0.271\\sqrt{2}$, $c:=\\cos(\\pi/8)=0.653\\sqrt{2}$）という状態を作るもののはずでした。実際に計算された終状態の状態ベクトルが一致しています。"
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22fec6ee",
+   "id": "1ad390a8",
    "metadata": {},
    "source": [
     "(other_gates)=\n",
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eed0ae95",
+   "id": "56c8fb1a",
    "metadata": {},
    "source": [
     "### 1、2量子ビット\n",
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25066215",
+   "id": "530fb802",
    "metadata": {},
    "source": [
     "### 3量子ビット以上\n",
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d605fb1",
+   "id": "5d942b74",
    "metadata": {},
    "source": [
     "## 量子状態生成\n",
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d9b603e",
+   "id": "b516feb6",
    "metadata": {},
    "source": [
     "### 問題1: 1量子ビット、相対位相付き\n",
@@ -337,7 +337,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e3ccabeb",
+   "id": "523ccf6f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "90ab3ec3",
+   "id": "bca2affe",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89a5f448",
+   "id": "340e264e",
    "metadata": {},
    "source": [
     "### 問題2: ベル状態、相対位相付き\n",
@@ -421,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3e525fdc",
+   "id": "06b13e1e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c7d0437",
+   "id": "c0d77eb9",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "340856e4",
+   "id": "95c016d9",
    "metadata": {},
    "source": [
     "### 問題3: GHZ状態\n",
@@ -497,7 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ee4f99d0",
+   "id": "2d85e588",
    "metadata": {
     "tags": [
      "remove-output"
@@ -536,7 +536,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd7ab5bc",
+   "id": "232e942d",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -558,7 +558,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "81e0d6ad",
+   "id": "028a3389",
    "metadata": {},
    "source": [
     "### 問題4: Equal superposition\n",
@@ -577,7 +577,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bd238fc2",
+   "id": "fec4cb9f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5052da7d",
+   "id": "25bac338",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3ca1727",
+   "id": "22f49c51",
    "metadata": {},
    "source": [
     "### 問題5: 特定の基底の符号を反転させる\n",
@@ -659,7 +659,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "298ee81c",
+   "id": "93e473ce",
    "metadata": {
     "tags": [
      "remove-output"
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ceb9cd1e",
+   "id": "f9937a5b",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55f7e59c",
+   "id": "002dd332",
    "metadata": {},
    "source": [
     "(equal_superposition_with_phase)=\n",
@@ -754,7 +754,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "eedd0204",
+   "id": "62e6011d",
    "metadata": {
     "tags": [
      "output_scroll",
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12579667",
+   "id": "2d915110",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -851,7 +851,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec628bcb",
+   "id": "d2839f31",
    "metadata": {},
    "source": [
     "## 量子計算プリミティブ\n",
@@ -861,7 +861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3d89cc1",
+   "id": "0911819d",
    "metadata": {},
    "source": [
     "### 条件分岐\n",
@@ -878,7 +878,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "54ff2e0c",
+   "id": "eeda02ad",
    "metadata": {},
    "outputs": [
     {
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7353b0fd",
+   "id": "68a97cea",
    "metadata": {},
    "source": [
     "測定オペレーションが入っているので、この回路をstatevector simulatorに渡すと、シミュレーションを実行するごとにレジスタ1の状態がランダムに決定されます。次のセルを複数回実行して、上の回路が狙い通り動いていることを確認してみましょう。\n",
@@ -929,13 +929,13 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a054c739",
+   "id": "2251ea22",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle | \\text{final} \\rangle =  - 0.383 |0:10\\rangle + 0.924 |1:10\\rangle$"
+       "$\\displaystyle | \\text{final} \\rangle =  - 0.556 |0:11\\rangle + 0.831 |1:11\\rangle$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -969,7 +969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ad399dde",
+   "id": "62e90c94",
    "metadata": {},
    "source": [
     "#### 条件分岐としての制御ゲート\n",
@@ -982,7 +982,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "897e99c4",
+   "id": "9a618dc1",
    "metadata": {},
    "outputs": [
     {
@@ -1015,7 +1015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "47201578",
+   "id": "fcd6c6a7",
    "metadata": {},
    "source": [
     "今度は全ての分岐が重ね合わさった量子状態が実現しています。"
@@ -1024,7 +1024,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "fd39438f",
+   "id": "3b7d9ebf",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94030f9a",
+   "id": "162efced",
    "metadata": {},
    "source": [
     "### 関数\n",
@@ -1071,7 +1071,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "1474bd80",
+   "id": "a4e3016e",
    "metadata": {},
    "outputs": [
     {
@@ -1111,7 +1111,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "0198b3d8",
+   "id": "f581a8ce",
    "metadata": {},
    "outputs": [
     {
@@ -1134,7 +1134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fc65f5b",
+   "id": "9e2d136c",
    "metadata": {},
    "source": [
     "入力レジスタの状態を色々変えて、状態ベクトルの変化を見てみましょう。\n",
@@ -1199,7 +1199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd55becc",
+   "id": "efaeda2f",
    "metadata": {},
    "source": [
     "#### SWAPテスト\n",
@@ -1217,13 +1217,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "f00c1d5a",
+   "id": "0b27513e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f980ec17f70>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f315ec3c700>"
       ]
      },
      "execution_count": 18,
@@ -1295,7 +1295,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "ad6ce041",
+   "id": "5e4b2186",
    "metadata": {},
    "outputs": [
     {
@@ -1316,7 +1316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6039d52",
+   "id": "a833db32",
    "metadata": {},
    "source": [
     "式{eq}`swap_test`を導いてみましょう。\n",
@@ -1368,7 +1368,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "1bce096f",
+   "id": "14fc7e94",
    "metadata": {},
    "outputs": [
     {
@@ -1392,7 +1392,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "90a99226",
+   "id": "ccfd39c0",
    "metadata": {},
    "outputs": [
     {
@@ -1415,7 +1415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "13157c01",
+   "id": "76ae8095",
    "metadata": {},
    "source": [
     "で、$|\\braket{\\psi}{\\phi}|^2$は"
@@ -1424,7 +1424,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "84846e2d",
+   "id": "c1846804",
    "metadata": {},
    "outputs": [
     {
@@ -1443,7 +1443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc1152b9",
+   "id": "bb2f41e5",
    "metadata": {},
    "source": [
     "それに対し上の回路を1000000回実行した時の$P_0 - P_1$は"
@@ -1452,14 +1452,14 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "90363e51",
+   "id": "9b2110b1",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.20437\n"
+      "0.203444\n"
      ]
     }
    ],
@@ -1475,7 +1475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92c145f3",
+   "id": "f07c77d8",
    "metadata": {},
    "source": [
     "です。"
@@ -1483,7 +1483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0ddd950",
+   "id": "3246df10",
    "metadata": {},
    "source": [
     "(inverse_circuit)=\n",
@@ -1504,7 +1504,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "35500122",
+   "id": "f75052dc",
    "metadata": {},
    "outputs": [
     {
@@ -1534,7 +1534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "826fd041",
+   "id": "944b0a6d",
    "metadata": {},
    "source": [
     "ポイントは`psi_circuit.inverse()`で得られる`psi_circuit`の逆回路です。逆回路とは、ゲートの順番を反転させ、全てのゲートをその逆操作で置き換えたものです。$\\ket{0}$から$\\ket{\\psi}$を作る回路を$U_{\\psi}$と表すと、逆回路は$U_{\\psi}^{-1}$もしくは$U_{\\psi}^{\\dagger}$と書かれ、\n",
@@ -1554,7 +1554,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56c59a33",
+   "id": "2565a9c0",
    "metadata": {},
    "source": [
     "```{toggle}\n",
@@ -1609,7 +1609,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1ddd9fc7",
+   "id": "c9665a66",
    "metadata": {},
    "source": [
     "上の回路で式{eq}`inverse_circuit`を確認しましょう。"
@@ -1618,14 +1618,14 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "8baac176",
+   "id": "379ebcfc",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.202728\n"
+      "0.203051\n"
      ]
     }
    ],
@@ -1640,7 +1640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c31be6f8",
+   "id": "89136ff2",
    "metadata": {},
    "source": [
     "## 複製不可能定理とテレポーテーション\n",
@@ -1658,7 +1658,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdcb27d0",
+   "id": "5e97c8e3",
    "metadata": {},
    "source": [
     "```{toggle}\n",
@@ -1686,7 +1686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f033bf4e",
+   "id": "cf48b77b",
    "metadata": {},
    "source": [
     "複製不可能定理は量子回路で実行できる計算のクラスを大幅に制限します。例えば、何かの計算を途中で打ち切ってその時点での量子状態をなんらかのメモリデバイスに保存できたとして、その状態をコピーすることができないので、結局そこから計算を再開できるのは一度だけということになります。\n",
@@ -1699,7 +1699,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "8a1a8a47",
+   "id": "e4c26a1a",
    "metadata": {},
    "outputs": [
     {
@@ -1753,7 +1753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a5ece7c",
+   "id": "fc4680e7",
    "metadata": {},
    "source": [
     "入力ビットの状態は"
@@ -1762,7 +1762,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "5eaaa1ff",
+   "id": "f6fdc4ba",
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +1785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79472db0",
+   "id": "24f2c997",
    "metadata": {},
    "source": [
     "回路の終状態は"
@@ -1794,13 +1794,13 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "f6b06d4f",
+   "id": "27756d3a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle | \\text{final} \\rangle = 0.939 |0:1:0\\rangle + 0.343e^{0.57 \\cdot \\pi i} |1:1:0\\rangle$"
+       "$\\displaystyle | \\text{final} \\rangle = 0.939 |0:0:0\\rangle + 0.343e^{0.57 \\cdot \\pi i} |1:0:0\\rangle$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1817,7 +1817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc41b8e9",
+   "id": "1abbf47f",
    "metadata": {},
    "source": [
     "上のセルを何度か実行すると、右二つのレジスタは実行するごとにランダムに違う値を取るものの、二つの項で共通で、一番左のレジスタが$\\ket{\\text{in}}$と同じ状態にあることがわかります。このようにテレポーテーションが実際に起こることを、上の回路を元に数式でも確認してみるといいでしょう。"
diff --git a/_sources/dynamics_simulation.ipynb b/_sources/dynamics_simulation.ipynb
index 86c47d3..883fc81 100644
--- a/_sources/dynamics_simulation.ipynb
+++ b/_sources/dynamics_simulation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9ce742f2",
+   "id": "e085b27c",
    "metadata": {},
    "source": [
     "# 物理系を表現する\n",
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3c9c7acd",
+   "id": "a8bace06",
    "metadata": {},
    "source": [
     "## 量子系のダイナミクスとは\n",
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c5cf268",
+   "id": "961d28a1",
    "metadata": {},
    "source": [
     "### 例：核磁気の歳差運動\n",
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6118715e",
+   "id": "b05e9e6d",
    "metadata": {},
    "source": [
     "### 量子コンピュータ上での表現\n",
@@ -136,7 +136,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "5de8b1bb",
+   "id": "1e43322d",
    "metadata": {
     "tags": [
      "remove-input"
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "09406c66",
+   "id": "6838dd93",
    "metadata": {
     "tags": [
      "remove-input"
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8eb27eb3",
+   "id": "6bf90828",
    "metadata": {},
    "source": [
     "### ハミルトニアンの対角化\n",
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9168a32",
+   "id": "2efa95f9",
    "metadata": {},
    "source": [
     "### 鈴木・トロッター分解\n",
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "372c9461",
+   "id": "7e9a09ba",
    "metadata": {},
    "source": [
     "### なぜ量子コンピュータが量子ダイナミクスシミュレーションに向いているか\n",
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36da7ded",
+   "id": "1738234d",
    "metadata": {},
    "source": [
     "## 実習：ハイゼンベルグモデルの時間発展\n",
@@ -410,7 +410,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b8717b86",
+   "id": "010f00c8",
    "metadata": {
     "tags": [
      "remove-input"
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c8b6658",
+   "id": "13cd38bb",
    "metadata": {},
    "source": [
     "によって、計算基底$\\ket{00}, \\ket{01}, \\ket{10}, \\ket{11}$はそれぞれ\n",
@@ -472,7 +472,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "1cabc183",
+   "id": "6bbf60d5",
    "metadata": {
     "tags": [
      "remove-input"
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bb9cccc",
+   "id": "8f4ebb93",
    "metadata": {},
    "source": [
     "最後に、$\\exp(\\frac{i \\omega \\Delta t}{2} \\sigma^Y_{j+1}\\sigma^Y_{j})$に対応する回路は"
@@ -514,7 +514,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "60b05376",
+   "id": "3bd96e0c",
    "metadata": {
     "tags": [
      "remove-input"
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "02f4dd9a",
+   "id": "80f2abd2",
    "metadata": {},
    "source": [
     "です[^sgate]。\n",
@@ -566,7 +566,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "84535fef",
+   "id": "a3a414b5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +585,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "b1f806f6",
+   "id": "a9e0b3f5",
    "metadata": {},
    "outputs": [
     {
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8fda33b2",
+   "id": "9963246a",
    "metadata": {},
    "source": [
     "量子回路シミュレーターで実行し、各ビットにおける$Z$方向スピンの期待値をプロットしましょう。プロット用の関数は比較的長くなってしまいますが実習の本質とそこまで関係しないので、[別ファイル](https://github.com/UTokyo-ICEPP/qc-workbook/blob/master/source/utils/dynamics.py)に定義してあります。関数はジョブの実行結果、系のスピンの数、初期状態、ステップ間隔を引数にとります。"
@@ -657,12 +657,12 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "dc029932",
+   "id": "02d35d60",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9321004",
+   "id": "edda09ae",
    "metadata": {},
    "source": [
     "ビット0でのスピンの不整合が徐々に他のビットに伝搬していく様子が観察できました。\n",
@@ -702,7 +702,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "29f8b655",
+   "id": "e3cad99b",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -747,7 +747,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "3fd3144a",
+   "id": "158212e5",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -780,7 +780,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "df6ea8ae",
+   "id": "edfff224",
    "metadata": {
     "tags": [
      "raises-exception",
diff --git a/_sources/dynamics_simulation.md b/_sources/dynamics_simulation.md
index 829d755..3260cd7 100644
--- a/_sources/dynamics_simulation.md
+++ b/_sources/dynamics_simulation.md
@@ -19,7 +19,7 @@ language_info:
   name: python
   nbconvert_exporter: python
   pygments_lexer: ipython3
-  version: 3.8.10
+  version: 3.10.6
 ---
 
 # 物理系を表現する
diff --git a/_sources/extreme_simd.ipynb b/_sources/extreme_simd.ipynb
index a2d3c17..86870f6 100644
--- a/_sources/extreme_simd.ipynb
+++ b/_sources/extreme_simd.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "01945ca5",
+   "id": "61b89efc",
    "metadata": {},
    "source": [
     "# 計算をする量子回路の実装"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28cc1cfe",
+   "id": "8861dae6",
    "metadata": {},
    "source": [
     "量子計算をする回路の感覚がつかめてきたところで、量子計算をするとはどういうことかに話を移して行きましょう。\n",
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "532c990a",
+   "id": "7344073f",
    "metadata": {},
    "source": [
     "## 量子計算の特徴\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4f9dccd4",
+   "id": "4963197b",
    "metadata": {},
    "source": [
     "(QFT)=\n",
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "62155749",
+   "id": "fab7b654",
    "metadata": {
     "tags": [
      "remove-output"
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "755be719",
+   "id": "4af88ed9",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "5af4a7d4",
+   "id": "6d0c0d67",
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "51b88284",
+   "id": "34c6d08d",
    "metadata": {},
    "source": [
     "上の回路でどうQFTが実現できるのか、初見で一目でわかる人は世界で数人しかいないでしょう。実際に状態を書き下しながら回路を追ってみましょう。\n",
@@ -317,7 +317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "25da3b79",
+   "id": "f07a99e9",
    "metadata": {},
    "source": [
     "(fourier_addition)=\n",
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "59398c17",
+   "id": "1aa4e3e9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e60d60b3",
+   "id": "21daf3dc",
    "metadata": {},
    "source": [
     "サブルーチンを具体的に数式で追ってみましょう。\n",
@@ -470,7 +470,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "71ddbd0a",
+   "id": "d7e1051b",
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "35897da5",
+   "id": "eec3225c",
    "metadata": {},
    "source": [
     "再び`statevector_expr`関数を使って終状態を確認してみましょう。"
@@ -538,7 +538,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "16aa6a80",
+   "id": "428629bd",
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4922f944",
+   "id": "bb6362c9",
    "metadata": {},
    "source": [
     "結果表示された状態は期待通り単一の計算基底$22:13:9$、つまりレジスタ1, 2, 3がそれぞれ$\\ket{9}, \\ket{13}, \\ket{22}$となっている状態です（回路全体でビットを右から書いて状態を表示するため、レジスタも右から左に並びます）。つまり、めでたく状態の変遷\n",
@@ -576,7 +576,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1574410f",
+   "id": "c41a370b",
    "metadata": {},
    "source": [
     "## 足し算の並列化\n",
@@ -593,7 +593,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "ba35c9b6",
+   "id": "b02abe27",
    "metadata": {},
    "outputs": [
     {
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "05306e4c",
+   "id": "38634465",
    "metadata": {},
    "source": [
     "2022年4月現在、IBMの持つ最大の量子コンピュータは127量子ビットです。このマシンを最大限利用するならば、$n_1 = n_2 = 42, n_3 = 43$で$2^{84}$通り、つまり約$2 \\times 10^{25}$（20𥝱）通りの足し算を同時に行うことができます。\n",
@@ -648,7 +648,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "bdcb9ad0",
+   "id": "5630c557",
    "metadata": {
     "tags": [
      "remove-output"
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dab383c7",
+   "id": "b33a52e7",
    "metadata": {},
    "source": [
     "回路の効率化とは具体的にどういうことでしょうか。もともとの回路と効率化したものとを比べてみましょう。まずは、単純にオペレーションの数を比較します。ゲート一つ一つで一定の確率でエラーが起こるということは、同じことをする回路ならゲートの数が少ないほうがより正確な計算をしてくれます。"
@@ -684,7 +684,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cc1205e3",
+   "id": "79a89e29",
    "metadata": {},
    "outputs": [
     {
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "145f49a0",
+   "id": "bef4eda8",
    "metadata": {},
    "source": [
     "効率化したはずの回路のほうがはるかにゲート数が多いという結果になりました。なぜでしょうか。"
@@ -711,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af162d4b",
+   "id": "bd939dfc",
    "metadata": {},
    "source": [
     "(transpilation)=\n",
@@ -755,7 +755,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e6e6dcf",
+   "id": "6ad6e29a",
    "metadata": {},
    "source": [
     "### 回路の比較\n",
@@ -770,7 +770,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "76a194cf",
+   "id": "76d6dc7e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -788,7 +788,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_111/1998159444.py:5: DeprecationWarning: The module 'qiskit.test.mock' is deprecated since Qiskit Terra 0.21.0, and will be removed 3 months or more later. Instead, you should import the desired object directly 'qiskit.providers.fake_provider'.\n",
+      "/tmp/ipykernel_110/1998159444.py:5: DeprecationWarning: The module 'qiskit.test.mock' is deprecated since Qiskit Terra 0.21.0, and will be removed 3 months or more later. Instead, you should import the desired object directly 'qiskit.providers.fake_provider'.\n",
       "  from qiskit.test.mock import FakeGuadalupe\n"
      ]
     }
@@ -817,7 +817,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "9152ae0b",
+   "id": "c5663df3",
    "metadata": {
     "tags": [
      "remove-output"
@@ -842,20 +842,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of operations in the original circuit: 460\n",
-      "  Breakdown: N(Rz)=187, N(X)=21, N(SX)=69, N(CNOT)=170\n",
-      "Number of operations in the optimized circuit: 383\n"
-     ]
-    },
-    {
-     "ename": "KeyError",
-     "evalue": "'x'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[11], line 20\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m  Breakdown: N(Rz)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_orig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrz\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(X)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_orig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(SX)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_orig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(CNOT)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_orig[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of operations in the optimized circuit: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcircuit_optimized_tr\u001b[38;5;241m.\u001b[39msize()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 20\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m  Breakdown: N(Rz)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_opt[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrz\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(X)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[43mnops_opt\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(SX)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_opt[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, N(CNOT)=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnops_opt[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcx\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mKeyError\u001b[0m: 'x'"
+      "Number of operations in the original circuit: 516\n",
+      "  Breakdown: N(Rz)=197, N(X)=8, N(SX)=110, N(CNOT)=188\n",
+      "Number of operations in the optimized circuit: 394\n",
+      "  Breakdown: N(Rz)=174, N(X)=1, N(SX)=64, N(CNOT)=142\n"
      ]
     }
    ],
@@ -884,7 +874,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "deffa267",
+   "id": "14f5cd1e",
    "metadata": {},
    "source": [
     "上のセルを実行すると、今度は効率化回路のオペレーションの全数が元の回路の8割、CNOTの数は6割という結果になることがわかります。\n",
@@ -896,14 +886,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "33ead6b0",
+   "execution_count": 12,
+   "id": "b549325f",
    "metadata": {
     "tags": [
      "remove-output"
     ]
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "simulator = AerSimulator()\n",
     "\n",
@@ -953,7 +954,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d96a2d2a",
+   "id": "1266df09",
    "metadata": {},
    "source": [
     "両方の回路とも、正しい足し算の式がランダムに出現していることを確認してください。"
diff --git a/_sources/grover.ipynb b/_sources/grover.ipynb
index 584f454..b52ec84 100644
--- a/_sources/grover.ipynb
+++ b/_sources/grover.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "f5b797c7",
+   "id": "9deb0a78",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "552ead12",
+   "id": "fb6f0620",
    "metadata": {},
    "source": [
     "ここでは、**グローバーのアルゴリズム**{cite}`grover_search,nielsen_chuang_search`の紹介と、そのアルゴリズムを使用して構造化されていないデータベースを検索する問題を考えます。アルゴリズムを説明した後に、Qiskitを使用してグローバーのアルゴリズムを実装します。\n",
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9b69717",
+   "id": "98cd1da9",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "492c48a4",
+   "id": "33645e66",
    "metadata": {},
    "source": [
     "(database)=\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e932a4eb",
+   "id": "08d3a4f6",
    "metadata": {},
    "source": [
     "(grover)=\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f0e3401",
+   "id": "1948dfac",
    "metadata": {},
    "source": [
     "(grover_phaseoracle)=\n",
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acd47d17",
+   "id": "7b927e20",
    "metadata": {},
    "source": [
     "(grover_circuit)=\n",
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "94f28d8c",
+   "id": "932e742f",
    "metadata": {},
    "source": [
     "(grover_superposition)=\n",
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a87ac34",
+   "id": "10aaa7a3",
    "metadata": {},
    "source": [
     "(grover_geometry)=\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "beb6b591",
+   "id": "e9bb75ad",
    "metadata": {},
    "source": [
     "(grover_oracle)=\n",
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2b3d6cf",
+   "id": "b39fe04c",
    "metadata": {},
    "source": [
     "(grover_diffuser)=\n",
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec7d7cd3",
+   "id": "d34b9523",
    "metadata": {},
    "source": [
     "(grover_amp)=\n",
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57d1dd04",
+   "id": "03c7ac5c",
    "metadata": {},
    "source": [
     "(grover_multidata)=\n",
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b20c5915",
+   "id": "c26e8420",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5f7caa12",
+   "id": "2ab39612",
    "metadata": {},
    "source": [
     "(imp_qiskit)=\n",
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "c82380eb",
+   "id": "5b9180bc",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a43aee72",
+   "id": "206cdefb",
    "metadata": {},
    "source": [
     "6量子ビットの回路`grover_circuit`を準備します。\n",
@@ -408,7 +408,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "58e1ac34",
+   "id": "f63d99d7",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -443,7 +443,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7fb72719af80>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f63b6946c50>"
       ]
      },
      "execution_count": 2,
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93dd7df2",
+   "id": "befb030f",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -516,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "779b9803",
+   "id": "549f1f5b",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -568,7 +568,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a359fdc",
+   "id": "a2ec4538",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -619,7 +619,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "b7e4c160",
+   "id": "32ad7f31",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -634,7 +634,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e24fcc3",
+   "id": "9cc899f5",
    "metadata": {},
    "source": [
     "正しく回路が実装できていれば、$\\ket{101101}=\\ket{45}$の状態を高い確率で測定できる様子を見ることができるでしょう。\n",
@@ -683,7 +683,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c2b6151",
+   "id": "9564339a",
    "metadata": {},
    "source": [
     "(imp_qc)=\n",
@@ -695,7 +695,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "194650d5",
+   "id": "34356396",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -745,7 +745,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "e293a53a",
+   "id": "e02b0293",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f2c19e68",
+   "id": "3454808d",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fa6beba1",
+   "id": "1d985489",
    "metadata": {},
    "source": [
     "シミュレータに比べると結果は非常に悪いですね。。。残念ながら、今の量子コンピュータをそのまま使うとこういう結果になってしまいます。しかし、{ref}`エラー緩和 <measurement_error_mitigation>`等のテクニックを使うことである程度改善することはできます。"
@@ -825,7 +825,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "31e4c842",
+   "id": "b3f4d222",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -843,7 +843,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4cb4605f",
+   "id": "4f4fab0e",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -878,7 +878,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "39310531",
+   "id": "7e4f786a",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -887,7 +887,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -905,7 +905,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "173f28be",
+   "id": "d7ee8dca",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -920,7 +920,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "2e58a98b",
+   "id": "6e860caa",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -929,7 +929,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -967,7 +967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b24afb0",
+   "id": "eae1ce35",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fad025c8",
+   "id": "8ec67cbe",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -991,7 +991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7be9b61d",
+   "id": "68ff1ff8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1009,7 +1009,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2e4b8e14",
+   "id": "b86798ad",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -1037,7 +1037,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1105,7 +1105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8dfd066",
+   "id": "24ae6a58",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/grover_number_light.ipynb b/_sources/grover_number_light.ipynb
index 833bd48..be98f86 100644
--- a/_sources/grover_number_light.ipynb
+++ b/_sources/grover_number_light.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "086573d8",
+   "id": "5cc6500d",
    "metadata": {},
    "source": [
     "# 【課題】ビット反転ボードの操作を見つける\n",
@@ -12,7 +12,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9dcab1fd",
+   "id": "5ab8d763",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0edc82c",
+   "id": "70886ff5",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -77,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4d5ec011",
+   "id": "6d1b6123",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "6780cee1",
+   "id": "b668242f",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d72bef56",
+   "id": "1de73ef5",
    "metadata": {},
    "source": [
     "以下の回路の中身を書いてください。"
@@ -131,7 +131,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "d2f0c917",
+   "id": "ae837ee5",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc07e103",
+   "id": "b3ccc1c3",
    "metadata": {},
    "source": [
     "コードをシミュレータで実行して、答えを確認する。\n",
@@ -167,7 +167,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "578dea73",
+   "id": "4dfa5fd4",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "efaf51ba",
+   "id": "59ba133e",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/more_dynamics.ipynb b/_sources/more_dynamics.ipynb
index 587307a..e4f1aad 100644
--- a/_sources/more_dynamics.ipynb
+++ b/_sources/more_dynamics.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "38c53819",
+   "id": "441b2e82",
    "metadata": {},
    "source": [
     "# 【課題】量子ダイナミクスシミュレーション・続\n",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "458b701f",
+   "id": "62982812",
    "metadata": {},
    "source": [
     "## 問題1: ハイゼンベルグモデル、X方向のスピン"
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "30f41ccd",
+   "id": "cc338207",
    "metadata": {},
    "source": [
     "### 問題\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "85e51e97",
+   "id": "d98bebaf",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0065b14f",
+   "id": "8edf27a8",
    "metadata": {
     "tags": [
      "remove-output"
@@ -75,7 +75,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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uy0SDpt3utgfTH3SrwJBdmt1hyAG1y7C4vpjs0myP+N/AE2h0Orfp6jmX4OnT4ZkVbVuhoqPdshXK3sHczhz0LUFHuJxGoyE5IoDkiACuGRYPQIPJzO6CKnbkVbAjr5ztxyqoamjEfGwzQT5n2ydFgeoCtXXKCcFNuJ+pSVNZNnFZm+62aP9ot+xuK6sv69LrRPcTPH06QVOmeEQrlP+okXjFxJyzu81/1Ein1SRBR7glPx8d6SnhpKeEA32xWBQOl9VSujkffjr344/mHiGx93i1q0v0OFOTpjIpcZJHdLfZOz3fnabxt+Yp3ZqezlNaoTQ6HdF/eFjtbtNo2u1ui/7Dw04NaRJ0hEfQajX0jw6i/7AhdgWdP6wpY/e6bxjbpxcTUiO4JDWSvpEBHQ9uFt2Op3S3jYgaQbR/9DnHFY2IGuGC6s6uvYHq0f7RPJT+kNu1nAnncbfuNhmMLIORPUurtX/aDkZW15qo8IpguuU5TtbbTm2PC/Hl0v6RXNY/knH9Igjxc589bUTP5mnT+EFmiolzc/Sgb5l1ZScJOh7IjrV/LANnkVNUzcbDJ9lwqIztxyowNZ1epVmn1TCidygTB0QxaUAUg2KDpLVHuJQnTeOXmWLCHUjQsZMEHQ/VybV/GkxmtuWeYv3Bk6w7WMrRsjqb+2OCfZk0MJJJA6K4JDUCfx/p1RXO5ynjXTx1vSLRvcj0ctG9pV2tTiG3c+0fPx8dEwdEMXFAFI+RxvHyetYdLGPd/lI2HTlJcbWBd7OO827WcfReWsb3i2DqoGimDIoiOti33ecUoqt5yrgimSkmPIkEHeG5LmDtn8Rwf34zNonfjE3C0Ghmy9FTfL+/lLX7SimobOC7/aV8t78UPoGhCSFMHxzDjMHR9I0MlC4u0eN5+kwx0bNI15V0XYlWFEXhQEkNa3JKWLOvlF3HK23u7xMRwLTB0cwcHMOwxFAJPaJH8sQVqEX3I2N07CRBR5xNaY2BtftK+XpvMZsPn8JkPj2gOTbElxmDY7h8SAyjkt10h3ohHMQTZ4qJ7kWCjp0k6Ah71Rga+eFgGav3FPP9/lKbndkjAvVcPiSGKy6KJT3lLKHHA3dcF6IjnjRTrDVPGfQtzk6Cjp0k6IjzYWg0s/HQSb7aU8y3OcVUG5qs97WEnqsujmV0cjjaltAjO66LbsjTQoMscth9SNCxkwQdcaEazRY2HT7Jqt1FfL23hKqG0zuxxwT7cuXFsdwc8hPJa+ehkR3XhXAZWeSwe5GgYycJOqIrmZosbD5yki9+LuLrvcXUGJrQYmGj/j5iNOW0v/OWRm3ZWbRburGEcBBZ5LD7sffzW3Y8FKIL+XhpmTggin/8cig/PjKV//xmJAtTy4jrMOSAzY7rQgiHyC7N7jDkgDqguri+mOzSbCdWJZxB1tERwkH0XjqmD45huiUYjp/7ekNFIb4pjq9LiJ5IFjnsuaRFRwhHC4y267K7PjnOfe/u5IeDZZgtPbpHWYguJ4sc9lzSoiOEoyWNU8fgdLjjOpRpItjYOADLT4V8/lMh0cF6Zg+P57oRCaRGBzm9ZCG6mxFRI4j2jz7nIocjoka4oDrhSNKiI4SjaXXqFHLAOsvKSoMGDZG/XMbH8ycwJyOJUH9vSqqN/PuHo0xbvp5rntvIW1vzbGZzCSE6R6fV8VD6Q8DpWVYtWo4fTH9QBiJ3QzLrSmZdCWexc8d1Y5OZ7/eX8tGOE3x/4HQ3lt5Ly8whMfxyZCLj+vY6vT6PEMJunrrIoWhLppfbSYKOcKpOrox8stbIpzsL+ODH4xwsqbWeTwjz4/pRifxyVAKxIX7OqFyIbsPTFjkU7ZOgYycJOsITKIrCzyeq+HDHcT7bVUhN80rMWg1c1j+SG0b3ZsqgKLx10hstRHck4awtCTp2kqAjPE2DyczqvUW8l3Wcbbnl1vORQXp+OTKBG0f3pncvfxdWKIToSrJtRfsk6NhJgo7wZLkn63h/+3E+2nGCk7VG6/lL+kVw05jeTEuLllYeITyYbFvRMQk6dpKgI7qDRrOFtftKeCfrOBsOldHyX3VkkJ4bRiVyY3oiCWHSyiOEJ5FtK85Ogo6dJOiI7uZ4eT3vbc/n/e2nW3k0Gpg0IIqbx/bmsv5R6GTGlhBub3vxdm77+rZzXvfqjFcZHTPaCRW5F3s/v2XBQCG6mcRwf34/YyCLpvbn25wS3t6Wx6bDp/hufynf7S8lIcyPX49J4vpRCfQK1Nv3pJ2cLSaEuHCybUXXkKAjRDflrdNyxUWxXHFRLEfLanlnWz4f7jjBiYoGnlq9n+XfHuSqi2OZMy6ZYYmhHT9Ru+v/xKmLILZa/0cI0bVk24quIV1X0nUlepAGk5mVPxfy1tY8fj5RZT0/NCGEORnJXHlxLL7erVpqcj6HD+bQduuK5q6v69+UsCOEg7SM0TnXthUyRkfG6JyVBB3RU/10vJI3t+Sx8udCTE0WAMIDfLhxdCI3j00iLtgHVgyxbcmxoVFbdhbtlm4sIRykZdYVYBN2ZNaVBB27SdARPd2pWiPv/3ict7fmU1DZAIBOq2FBShH3F2Se+wnmfgEpExxcpRA9l2xb0T4ZjCyEsEuvQD2/ndiPuyb0Yc2+Ut7YfIwtR0+Rm3sUfOx4gtr2p74KIbrG1KSpTEqc5HErI7vLas4SdIQQAHjp1E1DZw6JYX9xNeu/OQlH7XhgYLTDaxOip9NpdR41hdydVnOWJVOFEG0MjAnmrptvxhIUh0L7a+4oaNTd15PGObk6IYQ7axlXdOZCh6X1pWSuy2RN3hqn1iNBRwjRPq0O7eVPoYE2YceiqBuNPq27lW/3n8Ri6dFD/YQQzcwWM09mPdnuLLGWc09lPYXZYnZaTRJ0hBAdS7sarn8TTXCszekq7yjmN93Ps0Vp3Pnmj0x+eh3/3XKMBpPzfnkJIdxPdml2h1tWgBp2iuuLyS7NdlpNMkZHCHF2aVfDwCttVkYOSxrHo9Umem85xrvb8jl2qp5HP9vL098e5OYxSczJSCIq2NfVlQshnMwdV3OWoCOEODetrs0U8rhQPx6+fBD3TU7lox0neGVjLvnl9Tz3/WH+s/4o1wyL485L+9A/OshFRQshnM0dV3OWdXRkHR0huoTZovBtTjEvb8jlx7wK6/lJAyK589I+ZPTphUYjm4kK0Z05czVnWTDQThJ0hOh62fkVvLT+KKv3FtPyG2ZIfDB3X9qXy4fE4KWT4YFCdFfOWs1Zgo6dJOgI4Th5p+p4ZWMuH/x4HEOjus1EYrgfd1zSh1+OSsDfR3rPheiOnLGaswQdO0nQEcLxyutM/HdLHm9sOUZ5nQmAMH9v5mQkM3dcMuEB9izBLITwJI5eGVmCjp0k6AjhPA0mMx9ln+Cl9UfJL68HwM9bxw2jE7ljQgoJYf4urlAI4Skk6NhJgo4Qzme2KHy1p4gXfzjCnoJqQN1I9OqhcdxzWV8GxMhMLSHE2UnQsZMEHSFcR1EUNh0+xYs/HGHj4ZPW81MGRvHbSX0ZmRR+/k9uMdus/UPSOHWavBCiW5CgYycJOkK4h90nqnjxhyOs2lNknamVnhzOvEl9mdg/snNT03M+h9UPQnXh6XPBcTDzKXUBRCGEx7P389vt5ng+//zzJCcn4+vry5gxY8jKyjrr9StWrGDAgAH4+fmRmJjI/fffj8FgcFK1QoiuclFCCM//egRrMy/jxtGJeOs0ZB0r59bXtnPlPzfyxc+FmO3ZUyvnc/hgjm3IAaguUs/nfO6Yf4AQwi25VYvO+++/z5w5c3jxxRcZM2YMK1as4MMPP+TAgQNERUW1uf6dd97htttu49VXX2XcuHEcPHiQW265hRtvvJFly5bZ9ZrSoiOEeyquMvDKxqO8vS2f+uY9tFIiArjnsj5cOzwBH692/k6zmGHFkLYhx0qjtuws2i3dWEJ4OI/suhozZgyjR4/mueeeA8BisZCYmMi9997LQw891Ob6BQsWsG/fPtauXWs993//939s27aNjRs3tvsaRqMRo9FoPa6uriYxMVGCjhBuqqLOxBtbjvHapmNUNTQCEBfiy12X9uHG9N74ercKLLkb4I2rzv2kc79os6WFEMKzeFzXlclkYseOHUydenohIa1Wy9SpU9myZUu7jxk3bhw7duywdm8dPXqUVatWccUVV3T4OkuXLiUkJMR6S0xM7Np/iBCiS4UF+LBoan82PzSZP14xiKggPYVVBh5fmcMlT33Hv9YdpsagBiBqO9412Ya91wkhPJ7bBJ2TJ09iNpuJjo62OR8dHU1xcXG7j7npppv405/+xCWXXIK3tzd9+/Zl4sSJ/OEPf+jwdR5++GGqqqqst+PHj3fpv0MI4RgBei/uvLQP6x+YxF9mDyEhzI+TtSb+tvoA45/8juXfHqTWu5d9TxYYfe5rhBDdgtsEnfOxbt06nnjiCf71r3+RnZ3Nxx9/zJdffsmf//znDh+j1+sJDg62uQkhPIevt46bxybx/e8msuz6ofSNDKDa0MQzaw+R8U4D1T5RKHQ0Q0sDwfHqVHMhRI/gNhvNREREoNPpKCmxbVIuKSkhJiam3cc8+uij/OY3v+GOO+4A4KKLLqKuro677rqLP/7xj2i1Hp3jhBBn4a3T8v9GJDB7WDyr9xbz7HeH2VdUze+bbuIF7xWg4Yy403w080kZiCxED+I2ScDHx4eRI0faDCy2WCysXbuWjIyMdh9TX1/fJszodOovMDcaYy2EcCCtVsMVF8Wy6r5LeGXuKEripzOvcRFFyhmLDQbHwfVvyjo6QvQwbtOiA5CZmcncuXMZNWoU6enprFixgrq6Om699VYA5syZQ3x8PEuXLgVg1qxZLFu2jOHDhzNmzBgOHz7Mo48+yqxZs6yBRwjRM2g0GqYMimbywCg2HR7A/WunocnfQhSVnNKEkZA4md9GDyDJ1YUKIZzKrYLODTfcQFlZGY899hjFxcUMGzaM1atXWwco5+fn27TgPPLII2g0Gh555BEKCgqIjIxk1qxZ/PWvf3XVP0EI4WIajYZLUiO4JPUSth4dxLPfHWLT4VOwo4iPdhZzzbA4FkzqR5/IQFeXKoRwArdaR8cVZMFAIbq/HXkVPPvdIdYdKANAq4GrLo7j3sn9SI2WDUSF8EQeuWCgK0jQEaLn+Ol4Jc9+d4g1+0oB0GjgiotiuW9yquyYLoSHkaBjJwk6QvQ8ewqqePa7Q3y99/QszysuiuHeyakMipXfA0J4Agk6dpKgI0TPta+omme/O8Sq3acXJZ05OIb7pqSSFie/D4RwZxJ07CRBRwhxoLiGf353iFW7i2j5jThjcDT3TUllcFyIa4sTQrRLgo6dJOgIIVocKqnhn98d5oufC62BZ3paNAunSuARwt1I0LGTBB0hxJkOldTw7HeHWSmBRwi3JUHHThJ0hBAdOVxawz/X2gaeGYOjWTilv4zhEcLFJOjYSYKOEOJc2gs8MwfHsHBqF8/SspghbzPUlqg7rCeNk325hOiABB07SdARQtjrcGkNz6y1HcNz+RA18AyMucDfHzmfw+oHobrw9LngOJj5lOzPJUQ7JOjYSYKOEKKzDpbU8Mxa21laV14Uy8KpqfQ/n5WWcz6HD+YAZ/46bt5xXTYjFaINCTp2kqAjhDhfB4pr+OfaQ3y5uwhQV1q+8qJYFk1NpV+UnYHHYoYVQ2xbcmxo1JadRbulG0uIVuz9/NZ2eI8QQoizGhATxPO/HsFXCycwc3AMigJf/FzEtOXrWfTeTo6W1Z77SfI2nyXkAChQXaBeJ4ToNAk6QghxgQbFBvPib0by5X2XMD0tGkWBT3cVMnXZD2R+sItjJ+s6fnBtScf3nc91QggbEnSEEKKLDI4L4T9zRvHFvZcwdVAUFgU+zi5gyrIf+P2HP3G8vL7tgwKj7Xtye68TQtiQMToyRkcI4SA/Ha9kxZqDfH+gDAAvrYZfjkpkweR+xIf6qRdZx+gU0XYwMsgYHSHaJ2N0hBDCxYYmhvLarel8/NtxTEiNoMmi8G5WPhP//j2PfrqH4iqDGl5mPtX8CM0Zz9B8PPNJCTlCnCdp0ZEWHSGEk2w/Vs7ybw+y+cgpAHy8tPx6TG/mTexL1PFv2llHJ14NOTK1XIg2ZHq5nSToCCGcbcuRUyz/9iBZx8oB8PXW8puxSdw9IZmIUztkZWQh7CBBx04SdIQQrqAoCpsOn+Lpbw+wM78SAH8fHXPHJXPXhD6EBfi4tkAh3JwEHTtJ0BFCuJKiKKw7WMbybw/y84kqAAL1Xtw2PpnbJ/QhxM/bxRUK4Z4k6NhJgo4Qwh0oisKafaUs+/Yg+4qqAQjy9eLOCX24dXwyQb4SeIRoTYKOnSToCCHcicWi8PXeYpavOcjBEnVl5VB/b+66tA9zM5IJ0Hu5uEIh3IMEHTtJ0BFCuCOLReGL3UWsWHOQo2Xqysq9Any457K+3Dw2CT8fGaQsejYJOnaSoCOEcGdNZguf/1TIM2sPkXdKXVk5MkjP/Il9uTG9N77eEnhEzyRBx04SdIQQnqDRbOHj7BP8c+1hCiobAIgN8WX+pH5cPyoRHy8Xrf9qMasbjsqUeOFkEnTsJEFHCOFJTE0WPvjxOM99d5jiagMACWF+3Dcllf83PB4vnRMDT87n7SxyGKeu9CyLHAoHk6BjJwk6QghPZGg0825WPs9/f4STtUYAknv5s3BqKlcPjUenPXM7iS6W8zl8MIe2+3M1v+71b0rYEQ4lQcdOEnSEEJ6swWTmra15vPDDEcrrTAD0iwpk0dRUrhgSi9YRgce6EWlhBxfIRqTC8WRTTyGE6AH8fHTceWkfNjwwid/PGECInzeHS2tZ8M5OrvjnBlbvKabL/57N23yWkAOgQHWBep0QLiZBRwghuoEAvRfzJ/Vjw4OTWDQ1lSC9F/uLa7jnrR3Mem4j3+0v6brAU1vStdeJ7sdQDSd2wK534NvF0GR0WSmy8pQQQnQjwb7eLJran1vHpfDShqO8timXPQXV3Pb6jwxLDCVzWn8mpEag0VxAl1ZgdNdeJzxXYwOU7YeSvVCSA6U5UHYAas5o8bv4BohOc0mJMkZHxugIIbqx8joT/15/hDc359HQaAZgVFIYmdP6M65fxPk9qXWMThFtByODjNHppurLofhnKPoZin5Svz91GBRL+9cHRkNEf4gcAGN/C736dmk5MhjZThJ0hBA9QVmNkRd/OMJbW/MwNqkfTGP7hJM5bQDpKeGdf0LrrCuwDTsy66pbMNWrYaZgBxRmq18rjrV/rX8viB4MUYPVVpvIgRCRCn5hDi1Rgo6dJOgIIXqSkmoD//r+MO9mHcdkVgPPJf0iuH9af0YmdfKDqd11dOJh5pMScjxN1Qk4vg2OZ6lfi34Gxdz2urBkiB0KMRdD7DCIuQgCo+BCukLPkwQdO0nQEUL0RIWVDTz//WE++PE4jWb1Y+Cy/pHcP60/wxJD7X8iWRnZ8yiK2uWUt0n93y5vM1Qdb3tdYDTEj4L44RA/EuKGO7yVpjMk6NjJnjfKYrFgMpmcXFn35u3tjU4nvwyFcLXj5fU8//1hPtxxArNF/TiYMjCK+6f1Z0h8iIurE11CUaAiF3LXN982QF2p7TUando6kzgGEtPVryEJLmmpsZcEHTud640ymUzk5uZisXQw2Eqct9DQUGJiYi5s9ocQokvknarj2e8O83H2CZrzDtPSolk0NZXBcRJ4PE59ORxdB0e+g6M/QFW+7f06PSSMVlvgksap3+sDXVLq+ZKgY6ezvVGKopCfn09jYyNxcXFotbLsUFdQFIX6+npKS0sJDQ0lNjbW1SUJIZodLavl2e8O8+muAlo+HS4fEsPCqakMjJHufbdlMasDhg99o4abgmxsBolrvdQwk3IppFymdkV5+7qs3K4gQcdOZ3ujGhsbOXz4MHFxcYSEyF80Xe3UqVOUlpbSv39/6cYSws0cLq3hmbWH+eLnQmvgufKiWBZOTaV/dJBrixOq+nI4vFYNN4fXQEO57f1RadB3MvSZBEkZ4BPgmjodxN6gIwsGnoXZrI449/HxcXEl3ZO/vz+gBkoJOkK4l35RQTz7q+HcO7kfz6w5xJe7i/hydxGr9hRx1cVxLJzSj35RHhp4PHkAdXkuHFgFB75S/w2tZ0b5hqjBpt806DtJXctISNCxh4whcQx5X4Vwf/2jg3j+1yNYUFTNM2sOsXpvMSt/KuSLnwu5Zmgc901JpU+kB43taHdKfBzMfMo9p8QrChTvhn0r1VvZPtv7o9IgdTr0nwEJ6aCTj/UzSdfVWZq+DAYDubm5pKSk4Ovr2X2Z7kjeXyE8z97CKlasOcS3Oeo+VloNzB4Wz4LJ/dw/8FgXOTzzY8/NFjlUFDjxI+z7TA03rRfq0+ggeTwMuAIGXK6ua+OmzBaFrNxySmsMRAX5kp4Sjk7bdX/gSteVEEKILjc4LoSX5oxiT0EVK9YcZM2+Uj7eWcCnuwqYPTyeeyenkhLhhmNBLGa1JafdLSsUQAOrH4KBV7qmG0tR1BWI934Cez+1XdfGyw/6TYFBV0P/6W61lk1HVu8pYsnKHIqqDNZzsSG+LJ6Vxswhzp2AIkFHOIRGo+GTTz5h9uzZri5FCOEAQ+JDeHnuaH4+Uckzaw6xdn8pH2cX8NmuQmYPi+feyf1IdqfAk7fZtruqDQWqC9TrUiY4rSxK98HuD2H3R1CZd/q8T6DaHTXoakid5lEDiVfvKWLeW9ltImVxlYF5b2Xzws0jnBp2JOgIIYQ4bxcnhPLKLaP56Xglz6w9xHf7S/lf9gm1hcedAk9tSddedyEqj8Oej9RwU7Ln9Hlvf+g/EwZfq4Ybbz/H19LFzBaFJStzztZuxpKVOUxLi+nSbqyzkYVhuimLxcLSpUtJSUnBz8+PoUOH8tFHH6EoClOnTmXGjBm0DM8qLy8nISGBxx57DFBnm91+++3Wxw4YMIBnnnmmzWu8+uqrDB48GL1eT2xsLAsWLAAgOTkZgGuvvRaNRmM9FkJ0X0MTQ3n1ltF8Nn88kwdGYbYo/C/7BFOW/cD/ffATx07WubbAwOiuva6zjDWw8214/Sp15/c1j6shR+sNA66E616F3x+GX76mjhPywJADkJVbbtNddSYFKKoykJVb3uE1XU1adDqhZaE7V/D39+/ULKWlS5fy1ltv8eKLL5Kamsr69eu5+eabiYyM5I033uCiiy7in//8JwsXLuSee+4hPj7eGnQsFgsJCQl8+OGH9OrVi82bN3PXXXcRGxvL9ddfD8ALL7xAZmYmTz75JJdffjlVVVVs2rQJgO3btxMVFcVrr73GzJkzZeq4ED1IS+Bpr4XnmmFxrhvDkzROnV1VXUT743Q06v1J47ruNS0WOLYedr2jDipubPX5kXQJXPxLtWvK/zx2j3dTpTUdh5zzua4ryKyrTsy6qqurIzDQNbMKamtrCQiw75eD0WgkPDycNWvWkJGRYT1/xx13UF9fzzvvvMOHH37InDlzWLRoEc8++yw7d+4kNTW1w+dcsGABxcXFfPTRRwDEx8dz66238pe//KXd6+0ZoyOzroTo/n46XsmKNQf5/kAZ4OJZWtZZV2Abdrp41lVlvhpudr5tu/VCr34w9Ea4+AYI7X3hr+OGthw5xa9e2nrO6969cywZfXtd0GvZO+vK7bqunn/+eZKTk/H19WXMmDFkZWWd9frKykrmz59PbGwser2e/v37s2rVKidV654OHz5MfX0906ZNIzAw0Hp78803OXLkCAC//OUvufbaa3nyySf5xz/+0SbkPP/884wcOZLIyEgCAwP5z3/+Q36++h9saWkphYWFTJkyxen/NiGEZxmaGMprt6bz2fzxTBkYhUWBj3cWMHXZDyx6byeHS2ucV0za1WqYCT5jIGxw3IWHnCYT7PkY3rwGVlwM65aqIUcfAqNugzvWwoIf4dLfd9uQA5CeEk5siC8d9T9oUGdfpac4rxXLrbqu3n//fTIzM3nxxRcZM2YMK1asYMaMGRw4cICoqKg215tMJqZNm0ZUVBQfffQR8fHx5OXlERoa6pD6/P39qa2tdchz2/Pa9mqp8csvvyQ+Pt7mPr1eD0B9fT07duxAp9Nx6NAhm2vee+89fve73/H000+TkZFBUFAQf//739m2bRsAfn6e2XcshHCdoYnqoOXdJ6p4Zu0h1uwr4dNdhXz2UyFXXRzHfZP7keqMrSXSrlankHfVysgnD0P267DrXag/efp8ymUw/Dcw6CqPHW9zPnRaDYtnpTHvrWw0tNtuxuJZaU4biAxuFnSWLVvGnXfeya233grAiy++yJdffsmrr77KQw891Ob6V199lfLycjZv3oy3tzeAQwe+ajQau7uPXCktLQ29Xk9+fj6XXXZZu9f83//9H1qtlq+++oorrriCK6+8ksmTJwOwadMmxo0bx29/+1vr9S0tQQBBQUEkJyezdu1aJk2a1O7ze3t7W7fQEEKIFhclhPDyXHUdnn+uPcQ3OSXWlZavGBLLgsn9GBTr4M1DtboLm0JuboT9X8D2V+DYhtPnA2NgxG9g+M1dv5CfB21bMXNILC/cPKLNOjoxPX0dHZPJxI4dO3j44Yet57RaLVOnTmXLli3tPubzzz8nIyOD+fPn89lnnxEZGclNN93Egw8+2OEAWKPRiNFotB5XV1d37T/EDQQFBfG73/2O+++/H4vFwiWXXGIdLBwcHExERASvvvoqW7ZsYcSIEfz+979n7ty5/Pzzz4SFhZGamsqbb77J119/TUpKCv/973/Zvn07KSkp1td4/PHHueeee4iKiuLyyy+npqaGTZs2ce+99wJYg9D48ePR6/WEhbn/AldCCOcZEh/Cf+aMIqewmme/O8RXe4qt+2nNGBzNvZNTGRLvZpspV52AHW9A9hunp6FrtOreUiNvUbdicMQWDJ62bQVq2JmWFuPQlZHtpriJgoICBVA2b95sc/73v/+9kp6e3u5jBgwYoOj1euW2225TfvzxR+W9995TwsPDlccff7zD11m8eLGC2ppmc6uqqmpzbUNDg5KTk6M0NDRc2D/OBSwWi7JixQplwIABire3txIZGanMmDFDWbdunRIdHa088cQT1mtNJpMycuRI5frrr1cURVEMBoNyyy23KCEhIUpoaKgyb9485aGHHlKGDh1q8xovvvii9fljY2OVe++913rf559/rvTr10/x8vJSkpKS2q3Rk99fIUTX2l9Urcx/e4eS/NAXStKD6u2217KUnfkVri3MYlGUo+sV5b1fK8rjoYqyOFi9/a2foqz5k6JU5Dv29fd+piiLQ06/rvUWot72fubY13djVVVVHX5+t+Y2s64KCwuJj49n8+bNNjOFHnjgAX744Qfr+JDW+vfvb52509KCs2zZMv7+979TVFTU7uu016KTmJgoe125gLy/QogzHS6t4bnvDvP5T4VYmj+dJqRGcN+UVEYnO3Eatqkedn8A2/4DpXtPn0+6BEbfDgOvAi8fx9ZgMatr7nS4onPzlPhFu922G8uRPG6vq4iICHQ6HSUltqtSlpSUEBMT0+5jYmNj8fb2tummGjRoEMXFxZhMJnx82v4Q6vV664BcIYQQ7qVfVBArbhzOfVNS+de6I3yys4ANh06y4dBJxqSEc9+UVMb17dWpdcU6paoAtr8EP74Ghkr1nLe/OiU8/S6ITnPM67bHXbet8DBuM73cx8eHkSNHsnbtWus5i8XC2rVrbVp4Whs/fjyHDx/GYrFYzx08eJDY2Nh2Q44QQgjP0CcykH/8cijrfjeRX6X3xlunYVtuOb9+eRu/eGEz3+8vpUs7JE7sgI9ugxUXwcblasgJS4bpf4XMHJi1wrkhB9xr2woP5jZBByAzM5OXXnqJN954g3379jFv3jzq6uqss7DmzJljM1h53rx5lJeXs3DhQg4ePMiXX37JE088wfz58131TxBCCNGFEsP9Wfr/LuKH309ibkYSei8t2fmV3Pr6dq56diOr9xRhsZxn4LFYYP+X8OpMeHky7PkfKGa1e+rGd+DebBi3wHW7hbt624oLZTFD7gZ1T6/cDeqxC7hN1xXADTfcQFlZGY899hjFxcUMGzaM1atXEx2t/o+Yn5+PVns6myUmJvL1119z//33c/HFFxMfH8/ChQt58MEHXfVPEEII4QBxoX4suWYI8yf345UNufx3ax57C6u5561sUqMC+e2kvsy6OA4vnR1/vzc2wE/vwpbn4dRh9ZzWG4b8AsbOg7hhDv232M0V21Z0FTeaKeY2g5FdpTNbQIiuJe+vEOJ8VdSZeHVTLq9vPkaNoQmA3uH+3HNZX34xMh69VzuDcxsqYPvLsO3fUKduSYFv88rF6Xe3XTHZHThr24quZK35zHjRtTXbOxhZgo4EHZeR91cIcaGqDY38d0ser2zMpbzOBEB0sJ47J/ThpjG98ffxUgcYb/0X7HgdTM2r24f0hoz56uJ+etfsYWi3dltH4mHmk+4Xcpw4U8zjZl0JIYQQnRXs6838Sf24bXwK72bl85/1RymuNvCXL/ex8rsNPBG1hrSyr9BYGtUHRA+B8Ytg8LWOWdzPEbp62wpHcsOZYh7yv7IQQgjRMT8fHbddksKvx/bmu3Xf4bNlBRObNqErUTst8oJGEDL194RefDk4amq6I13othXO4oYzxSToCCGE6B5O7EC//u9cfvAr9VgDW71G87e6K8k29MfnQw3XHd3D3Zf2IamX++9b6JHccKaYW00vF11j4sSJLFq0qMP7k5OTWbFihdPqEUIIh8rfBm/9Qp0ifvArQAOD/x/cs5Exf/yW+XNuYmRSGKYmC+9sy2fSP9Zx77s72VtY5erK7Wa2KGw5corPdhWw5cgpzOc7pd7RWmaK0VGrmUYdX+TEmWLSouMEZoviHhubNdu+fbvNLuwajYZPPvmE2bNn21y3bt06MjMz2bt3L4mJiTzyyCPccsstzi1WCCE6krcZ1j0JuT+oxxqduoLxhP+DiH7qKWDKoGgmD4wiK7ecF344wroDZaz8qZCVPxVyWf9I5k3sy5iUcMettnyBVu8parMTeKyLdgI/J61OnUL+wRzUd7+dmWIzn3Tq+CIJOg7mjj+gkZGR57wmNzeXK6+8knvuuYe3336btWvXcscddxAbG8uMGTOcUKUQQnQgfyt8/8TpgKP1gmE3wSWZEJ7S7kM0Gg1j+vRiTJ9e7C2s4sUfjvLlz4X8cLCMHw6WMSwxlHsu68v0tGi0LvxD9Eyr9xQx763sNhO1i6sMzHsrmxduHuF+YSftanUKebvr6Dh/pphML3fg9PKOfkBb/hNy1A/oxIkTGTJkCAD//e9/8fb2Zt68efzpT39Co9GQnJzMokWLWLRoEcnJyeTl5Vkfm5SUxLFjx3jwwQf58ssv2bNnj/W+G2+8kcrKSlavXt0ldcr0ciFEp+Rvg3VL4ej36rHWG4b/Wm3BCe3d6afLO1XHSxuO8sGPJzA1qVsJ9YkM4O5L+zB7eAdr8TiR2aJwyVPf2fyh3JoGiAnxZeODk13aS9Ahi9mhM8XsnV4uY3QcxGxRWLIyp921LFvOLVmZ47B+1jfeeAMvLy+ysrJ45plnWLZsGS+//HKb67Zv3w7Aa6+9RlFRkfV4y5YtTJ061ebaGTNmsGXLFofUK4QQHSrcCW9dB69OV0OO1gtGzIX7smHWM+cVcgCSegXwl9kXsenByfx2Yl+CfL04WlbHg//bzYSnvufFH45QbWjs4n+M/bJyyzsMOaB+lhRVGcjKLXdeUZ3RMlPsouvUry6aDi9dVw7SmR/QjL69uvz1ExMTWb58ORqNhgEDBrB7926WL1/OnXfeaXNdSzdWaGiozS7xxcXF1q03WkRHR1NdXU1DQwN+fn5dXrMQQtgo2at2Ue3/Qj3W6NQuqkt/p2642UUig/Q8MHMgv53Uj3e35fPKxlyKqw08+dV+nvvuMDeN6c2t45OJDXHu773Smo4/Q87nup5Kgo6DuPoHdOzYsTYD6zIyMnj66acxm12zqZoQQtjt1BE14Oz5H6CARgsXXQ+XPQC9+jrsZQP1Xtx5aR/mjkvms10FvLThKAdLavnP+qO8ujGXq4fFcdelfRgY03E3SVeKCrKvS9/e63oqCToO4uk/oDExMZSU2C7oVFJSQnBwsLTmCCEco7oQfngKsv+r7iIO6grGEx+GyAFOK8PHS8svRyXyixEJrDtYyr9/OMq23HI+zi7g4+wCLu0fyV0T+jC+Xy+HztRKTwknNsSX4ipDR1t6EhOizuQVHZOg4yCu/gHdtm2bzfHWrVtJTU1Fp2vbR+rt7d2mpScjI4NVq1bZnPv222/JyMjo+mKFED1bfTlsXAZZL0FTcyt36nSY/CjEXuyysrRaDZMHRjN5YDS7jlfyn/VHWL2nmPUHy1h/sIxBscHcOSGFqy6Ow8er64e86rQaFs9KY95b2R1N1GbxrDT3HIjsRmQwsoO0/IBC22WTnPEDmp+fT2ZmJgcOHODdd9/l2WefZeHChe1em5yczNq1aykuLqaiogKAe+65h6NHj/LAAw+wf/9+/vWvf/HBBx9w//33O6ReIUQPZKqD9f+AZ4bC5mfVkNN7HNy6Gn79oUtDzpmGJYbyr1+PZN3vJnHLuGT8vHXsK6om84OfuPRv3/PCuiNU1Xf9wOWZQ2J54eYRxITYtv7HhPi659RyNyTTyx28e7kr1tGZOHEigwcPxmKx8M4776DT6Zg3bx5/+ctf2kwvB1i5ciWZmZkcO3aM+Ph4jh07BqgLBt5///3k5OSQkJDAo48+2qULBsr0ciF6KHMT7PyvuthfbbF6LvoimPIYpE7ziL2oKutNvL0tn9c3H6OsxgiAv4+O60clctv4FHr38u/S13O3hWfdgb3TyyXoODjogPyAdkSCjhA9jKLA/i9hzeNw6pB6LrS32kU15DrQel4ng7HJzMqfinh5w1H2F9cAak6bnhbN7Zf0YXRymNuuuOzp7A06MkbHCXRajUOmkAshhMc4ngXfPArHt6rH/r3g0gdg1K3gpXdtbRdA76XjupEJ/GJEPBsPn+SlDbmsP1jG13tL+HpvCRfFh3D7JSlccVGsQ8bxiHOToCOEEMJxTh1RW3D2fa4ee/lBxnwYvxB8nTNN2xk0Gg0TUiOZkBrJoZIaXt2Uy8fZBewuqGLR+7tY+tU+5mQk86v03oQH+Li63B5Fuq6c0HUl2ifvrxDdWH05/PA32P4SWJrUtXCG/Rom/aF5d+vu71StkXe25fPm1jzrOB69l5Zrh8dz6/gUBsQEubhCzyZdV0IIIZyvyahOE1//NzBUqef6TYNpf4LoNNfW5mS9AvXcOyWVuy/ry5e7C3l14zF2F1Tx3vbjvLf9OOP69uKWcclMGRQt4zYdSIKOEEKIC6cosG8lfPsoVBxTz0UPgel/hr6TXVqaq/l4abl2eAKzh8WzI6+CVzflsnpPMZuPnGLzkVMkhPkxNyOZ60clEuLv7epyux0JOkIIIS5M4S74+o+Qt1E9DoyByY+o+1K5aCNHd6TRaBiVHM6o5HAKKhv475Y83tuez4mKBv66ah/Lvj3I7OHxzB2X5LRtJnoCCTpCCCHOT00xrP0z7HobUMDLF8bdpw401ge6ujq3Fh/qx0OXD2ThlFQ+21XA65uPsb+4hnez8nk3K5+xfcKZm5HMtLRovHQyW+tCSNARQgjROY0G2Po8bFgGplr13EW/hCmLITTRtbV5GD8fHTem9+aG0Ylsyy3nzS3H+HpvCVuPlrP1aDkxwb7cNKY3N6Ynuu3eiO5Ogo4QQgj7tIzD+eYRqMxTz8WPgplPQuJo19bm4TQaDWP79GJsn14UVjbw9rY83ss6TnG1gWXfHuTZ7w4xc0gsczKSGJUkixB2hrSHdUMTJ060bu/QnuTkZFasWOG0eoQQ3UDJXnhjFnzwGzXkBMXCtf+B27+VkNPF4kL9+P2MgWx+eDIrbhjGyKQwGs0KK38q5JcvbmHmig38d2setcYmV5fqESToOIPFDLkbYPdH6leL+dyPcaDt27dz1113WY81Gg2ffvqpzTVFRUXcdNNN9O/fH61We9bgJIToxurLYdXv4cVL4NgG0Onh0t/Dgh9h6A0euW2Dp9B76Zg9PJ7/zRvHF/dewo2jE/Hz1nGgpIZHP93DmL+u4Y+f7CansNrVpbo16bpytJzPYfWDUF14+lxwHMx8CtKudklJkZGR57zGaDQSGRnJI488wvLly51QlRDCrVjMkP2GOti4oVw9N2gWTP8LhCW7tLSeaEh8CE/+4mIevmIQH2ef4K2teRwpq+Ptbfm8vS2f4b1D+fWYJK66OBZfb5np1ppEcUfK+Rw+mGMbcgCqi9TzOZ877KWbmppYsGABISEhRERE8Oijj9KyCHbrrqvk5GQArr32WuvO5i3nn3nmGebMmUNISIjD6hRCuKH8bfCfy+CL+9WQEzkI5nwGN7zVLUKO2aKw5cgpPttVwJYjpzBbPGeDgBA/b24dn8KazMt4584xXHlxLF5aDTvzK/ndhz8x5om1LFm5l4MlNa4u1W1Ii46jWMxqSw7t/QekABpY/RAMvNIh60y88cYb3H777WRlZfHjjz9y11130bt3b+68806b67Zv305UVBSvvfYaM2fORKeTvwSE6LFqSmDNYvjpXfVYH6Ju2TD6dtB1j4XsVu8pYsnKHIqqDNZzsSG+LJ6VxswhsS6srHM0Gg3j+kYwrm8EpTUGPvzxBO9sy6egsoHXNh3jtU3HGJUUxq/Se3NlD2/lkaDjKHmb27bk2FCgukC9LmVCl798YmIiy5cvR6PRMGDAAHbv3s3y5cvbBJ2WbqzQ0FBiYmK6vA4hhAcwN0LWf+D7pWBqbgkY/ht1unjgubu6PcXqPUXMeyu7zZ+fxVUG5r2VzQs3j/CosNMiKsiX+ZP6cc9lfVl/qIx3t+Wzdn8pP+ZV8GNeBUtW7mX28HhuHN2btLietxChBB1HqS3p2us6aezYsTbTDzMyMnj66acxm107EFoI4WaObYJVv4PSHPU4bjhc8TQkjHRtXV3MbFFYsjLnbG3sLFmZw7S0GI/dd0qn1TBpQBSTBkRRUm3gwx/VPbVOVDTw5pY83tySx8UJIdw4ujezhsYS5Ns9WunO5YKDTnl5OaGhoWhl5L2twOiuvU4IIbpSTTF88yjs/kA99guHqYth+JxuOZMqK7fcprvqTApQVGUgK7ecjL69nFeYg0QH+7Jgciq/ndiPTUdO8l7Wcb7JKebnE1X8fGI3f/4ihysuiuWG0YmMTu7e6/KcV9DJycnh888/5/PPP2fbtm2EhYVxxRVXcM011zBz5kwCAgK6uk7PkzROnV1VXUT743Q06v1J4xzy8tu2bbM53rp1K6mpqe2OwfH29paWHiF6CnNTczfVE83dVBoYdStMfhT8w11dncOU1nQccs7nOk+h1WqYkBrJhNRITtUa+Ti7gPe253OkrI7/ZZ/gf9knSIkI4JejEvjFiASig7vf6st2x/YDBw7wf//3f6SmpjJ27Fi2b9/OPffcQ0lJCatWrSIpKYk//elPREREcPnll/PCCy84sm73p9WpU8gBtVG0tebjmU86bMO7/Px8MjMzOXDgAO+++y7PPvssCxcubPfa5ORk1q5dS3FxMRUVFdbzu3btYteuXdTW1lJWVsauXbvIyclxSL1CCCfI36rOpvr6YTXkxI+EO7+Dq5Z365AD2L19QnfeZqFXoJ47L+3DmszL+N+8DG4YlYi/j47ck3X8bfUBMpau5dbXsvjy5yKMTd3nj1+N0jLn+Bxee+01tm3bxjXXXMOUKVPw8fFp97pjx47x2WefsXLlStasWdOlxTpCdXU1ISEhVFVVERxsO0jLYDCQm5tLSkoKvr7n+cPf7jo68WrIcdA6OhMnTmTw4MFYLBbeeecddDod8+bN4y9/+Yt1CvmiRYusiwCuXLmSzMxMjh07Rnx8PMeOHQNotykzKSnJev+F6pL3VwhxbnWnYM1jsPMt9dgvDKY+3m27qdpjtihc8tR3FFcZOmpjJybEl40PTvbYMTrno87YxJc/F/HhjuNsP3b6D91Qf2+uHhrHL0YkcHFCiFt2bZ3t87s1u4PO2WRnZzNixIgLfRqXcHjQAXWqed5mdeBxYLTaXeWglhxPIkFHCAezWGDnm7DmcWho/hAb/huYugQCPH8cSme1zLoC2wEFLR/hnjrrqqvknqzjox3H+d+OAoqrT3fh9YsK5BcjErh2eDwxIe7zu9qpQcfLy4v77ruPZcuWWc+tWrWKK6644kKf2uGcEnREu+T9FcKBiveoC/6dyFKPoy+CK5+G3mNcW5eLdZd1dBzJbFHYePgk/9txgq/3FmNssgCg1cD4fhFcOzyeGYNjCNC7duK2U4PO8OHDueaaa8jLy+O1114DYMSIEWRnZ1/oUzucBB3XkfdXCAcw1sC6J2HrC6CYwScQJv0R0u8CnawoAuoHeVZuOaU1BqKCfElPCe9R3VWdUW1oZNXPRfwv+4RN15a/j46Zg2O4dkQ84/pGuOT9szfodMlPvUaj4fHHH2fFihVcd911vPvuu3RBfhJCCNEZ+76Arx5QFyMFSLsGZiyFkHjX1uVmdFpNt5hC7gzBvt7cmN6bG9N7k3eqjk92FvDJzgLyTtXz8c4CPt5ZQGSQnlkXxzF7eBwXxbvfeJ4uCTotSWrRokWEhYVx9dVX09DQ0BVPLYQQ4lwqj6sB58Aq9Tg0Se2mSp3m2rpEt5LUK4BFU/uzcEoq2fmVfLLzBF/8XERZjZFXN+Xy6qZc+kQGcM3QeK4eFkdKhHssNdMlXVdn+uSTT7j99tspLy/v6qfuctJ15Try/gpxgcxNsO0FdeuGxjrQesP4++DS34O3n6urEz2AqcnC+oNlfLqrgG9zSqzjeQAuTgjh6qFxXHVxnEMGMTtkjM7evXvR6/X069evS4p0BxJ0XEfeXyEuwIkdsHIhlOxWj3uPU9fDiRro2rpEj1VjaOSbvSV8/lMhGw+ftO4Kr9HAq3NHM2lgVJe+nkPG6GRmZjJ48GCb2VVffvkl77zzDlFRUSxcuJDk5OTzLloIIcQ5GKrhu7+oqxujqGviTPszDPt1j1kTR7inIF9vfjEygV+MTOBUrZFVu4v4/KdCdhdUMSo5zGV1dSro/PTTTzz22GPW43379nHttdcSFRWF0Wjk7bffZteuXcTFxXV5oUII0aMpCuxbqY7FqSlSz118I8z4KwREuLY2Ic7QK1DPbzKS+U1GMpX1JpduINqp+F9VVUViYqL1+M0336RPnz7k5eVx4sQJhg4dypNPPtnlRQohRI9WVQDv/Ro++I0acsL7wJzP4P/9W0KOcHuh/u3vpOAsnQo6CQkJFBUVWY/Xrl3LL3/5S3Q6HXq9nocffphvvvmmy4sUnTNx4kTr9g7tSU5OZsWKFU6rRwhxnixm2PZveD4dDnwJWi+Y8DuYtxn6THR1dUJ4hE4FnalTp1rH5+Tl5ZGdnc306dOt9/ft25fjx493bYXdgNliZnvxdlYdXcX24u2YLa7dLG379u3cdddd1mONRsOnn35qc83HH3/MtGnTiIyMJDg4mIyMDL7++msnVypED1a8B16ZrnZVmWohIR3u3gBTHpUZVUJ0QqfG6DzyyCMMHz6cPn36YDAYSExM5JJLLrHeX1JSQmBgYJcX6cnW5K3hyawnKakvsZ6L9o/mofSHmJo01SU1RUZGnvOa9evXM23aNJ544glCQ0N57bXXmDVrFtu2bWP48OFOqFKIHqqxAX74G2z+J1iaQB+sbsA58lYZbCzEeejUfzXx8fFs376da6+9lssvv5yPP/7YZgXE7777jv79+19QQc8//zzJycn4+voyZswYsrKy7Hrce++9h0ajYfbs2Rf0+l1pTd4aMtdl2oQcgNL6UjLXZbImz3G7uzc1NbFgwQJCQkKIiIjg0Ucfta5W3brrqmWW3LXXXmvd2RxgxYoVPPDAA4wePZrU1FSeeOIJUlNTWblypcNqFqLHy10PL4yDjcvUkDNoFszPgtG3S8gR4jx1+r+cpKQknn76aV555ZU2f9nn5OTwi1/84ryLef/998nMzGTx4sVkZ2czdOhQZsyYQWlp6Vkfd+zYMX73u98xYcKE837trma2mHky60kU2i5T1HLuqaynHNaN9cYbb+Dl5UVWVhbPPPMMy5Yt4+WXX25z3fbt2wF47bXXKCoqsh6fyWKxUFNTQ3h4uEPqFaJHqy+Hz+bDG7Og/CgExcINb8MNb0GwbDQpxIWwO+gUFxdjNBrPes2bb75pHQR79OjRThezbNky7rzzTm699VbS0tJ48cUX8ff359VXX+3wMWazmV//+tcsWbKEPn36nPM1jEYj1dXVNjdHyC7NbtOS05qCQnF9Mdmljtn4NDExkeXLlzNgwAB+/etfc++997J8+fI217V0Y4WGhhITE9Nht9Y//vEPamtruf766x1SrxA9kqLAno/VwcY731LPjbod5m+DQVe5tjYhugm7g85HH31EeHg41157La+99hplZWVtrtm2bRt/+MMfGDx4MEOHDu1UISaTiR07djB16ulxK1qtlqlTp7Jly5YOH/enP/2JqKgobr/9drteZ+nSpYSEhFhvrafLd6Wy+rbvz4Vc11ljx4616VbMyMjg0KFDmM2db0F65513WLJkCR988AFRUV27sqUQPVZVAbz7K/joVqgrg4j+cOtquGoZ+Ia4ujohug27g86CBQv46aefmDBhAq+//joJCQlccsklPPHEE9x5553ExsYye/ZsSktLefLJJ9sNQmdz8uRJzGYz0dHRNuejo6MpLi5u9zEbN27klVde4aWXXrL7dR5++GGqqqqsN0fNEov0P/eA385c5yrvvfced9xxBx988IFNCBVCnCeLBbJegufHwMGv1P2pLnsI7tkISRmurq7TzBaFLUdO8dmuArYcOWVd9l8Id9GpWVf9+vUjMzOTzMxMTp06xRdffMGqVatITk7mf//7HxkZGU7bnr2mpobf/OY3vPTSS0RE2L9gll6vR6/XO7Ay1YioEUT7R1NaX9ruOB0NGqL9oxkRNcIhr79t2zab461bt5KamopOp2tzrbe3d7stPe+++y633XYb7733HldeeaVD6hSiRyk7CCvvg/zmVuqE0XD1sxA1yLV1nafVe4pYsjKHoiqD9VxsiC+LZ6Uxc4iMLRLuoVNBp7VevXoxd+5c5s6d2yWFREREoNPpKCmxHddSUlJCTExMm+uPHDnCsWPHmDVrlvWcxaLumurl5cWBAwfo27dvl9R2PnRaHQ+lP0Tmukw0aGzCjgY1DD6Y/iA6bdvg0RXy8/PJzMzk7rvvJjs7m2effZann3663WuTk5NZu3Yt48ePR6/XExYWxjvvvMPcuXN55plnGDNmjLVVzc/Pj5AQaVYXolPMjbBphTpt3GwC7wCYuhhG3wEO+h3gaKv3FDHvrew2f8YVVxmY91Y2L9w8QsKOcAtuM1/Rx8eHkSNHsnbtWus5i8XC2rVrycho25w7cOBAdu/eza5du6y3q6++mkmTJrFr1y6Hjb3pjKlJU1k2cRlR/rbjWqL9o1k2cZlD19GZM2cODQ0NpKenM3/+fBYuXGizSGBrTz/9NN9++y2JiYnWmXT/+c9/aGpqYv78+cTGxlpvCxcudFjNQnRLBdnwn4nqRpxmE/Sbpg42HnO3x4Ycs0VhycqcdtqqsZ5bsjJHurGEW9AoLYuruIH333+fuXPn8u9//5v09HRWrFjBBx98wP79+4mOjmbOnDnEx8ezdOnSdh9/yy23UFlZ2WaV37M52zbvBoOB3NxcUlJS8PX1Pe9/l9liJrs0m7L6MiL9IxkRNcJhLTmepKveXyHckqke1j0BW54HxQJ+4XD5U3DRL8FJXfyOsuXIKX710tZzXvfunWPJ6NvLCRWJnuhsn9+tnXfXlSPccMMNlJWV8dhjj1FcXMywYcNYvXq1dYByfn4+Wg9cNEun1TE6ZrSryxBCOEvuevj8PqjIVY8v+iXMfLLbbMBZWmM490WduE4IR3KroAPq7K4FCxa0e9+6devO+tjXX3+96wsSQgh7NVTCt49B9hvqcXA8XLUc+s9waVldLSrIvhZYe68TwpHcLugIIYRH2r8KvsyEmiL1ePQdMGUx+HbcpO6p0lPCiQ3xpbjK0O44HQ0QE+JLeoqspC5cz/P6gYQQwp3UlsGHt8J7v1JDTnhfuGUVXPl0tww5ADqthsWz0gA4c7RRy/HiWWnotJ49Fkl0DxJ0hBDifCgK/PyBun3D3o9Bo4Pxi2DeJkge7+rqHG7mkFheuHkEMSG23VMxIb4ytVy4Fem6EkKIzqo6AV9kwqGv1ePoIXDNcxA3/OyP62ZmDollWloMWbnllNYYiApSu6ukJUe4Ewk6QghhL4sFsl+Hbx4DUw3ofOCyB9SWHJ23q6tzCZ1WI1PIhVuToCOEEPY4dQRWLoRjG9TjhNFw9XMQNdC1dQkhzkqCjhBCnI3FDFv/Bd/9FZoawNsfpjwG6Xd57MrGQvQkMhjZCRSzmbptWVR98SV127JQ2tlAsytNnDiRRYsWdXh/cnIyK1ascGgNQnQLpfvglenwzSNqyEm5DOZthrHzJOQI4SGkRcfBqr/5hpInltLUvCkmgFdMDNF/eJjg6dNdUtP27dsJCAiwHms0Gj755BNmz55tPbdx40YefPBB9u/fT319PUlJSdx9993cf//9LqhYCCczN8LG5bD+7+r+VPpgmP4XGDHH47dvEKKnkaDjQNXffEPBwkXqNNRWmkpK1PPPrHBJ2ImMjDznNQEBASxYsICLL76YgIAANm7cyN13301AQECHm4MK0S0U7oTPFkDJHvW4/+Vw1TIIjnNtXUKI8yJdVw6imM2UPLG0TchR71TPlTyx1GHdWE1NTSxYsICQkBAiIiJ49NFHadm/tXXXVXJyMgDXXnstGo3Gejx8+HB+9atfMXjwYJKTk7n55puZMWMGGzZscEi9QrhcYwN8uxhemqKGHP9e8ItX4FfvSsgRwoNJ0HGQ+h932HRXtaEoNBUXU//jDoe8/htvvIGXlxdZWVk888wzLFu2jJdffrnNddu3bwfgtddeo6ioyHp8pp07d7J582Yuu+wyh9QrhEvlbYEXL4FNK0Axw5BfwPwsuOg66aoSwsNJ15WDNJWVdel1nZWYmMjy5cvRaDQMGDCA3bt3s3z5cu68806b61q6sUJDQ4mJiWnzPAkJCZSVldHU1MTjjz/OHXfc4ZB6hXAJYy2sXQJZLwEKBMao3VQDr3R1ZUKILiJBx0G87BgH05nrOmvs2LFoWv0lmpGRwdNPP425k11lGzZsoLa2lq1bt/LQQw/Rr18/fvWrX3V1uUI435Hv4POFUJWvHg+/Gab/FfxCXVqWEKJrSdBxEP9RI/GKiaGppKT9cToaDV7R0fiPGun84johJSUFgIsuuoiSkhIef/xxCTrCszVUwjd/hJ1vqcchveHqZ6DvZJeWJYRwDBmj4yAanY7oPzzcfHBGH3/zcfQfHkajc8xaHNu2bbM53rp1K6mpqejaeT1vb2+7WnosFgtGo7HLahTC6fZ/Cc+PaQ45GnXRv99ukZAjRDcmLToOFDx9Ojyzou06OtHRDl9HJz8/n8zMTO6++26ys7N59tlnefrpp9u9Njk5mbVr1zJ+/Hj0ej1hYWE8//zz9O7dm4ED1eXt169fzz/+8Q/uu+8+h9UshMPUnYRVv1d3GQfo1U/dviEpw7V1ncFsUWSDTCG6mAQdBwuePp2gKVPUWVhlZXhFRuI/aqTDWnJazJkzh4aGBtLT09HpdCxcuLDD9W+efvppMjMzeemll4iPj+fYsWNYLBYefvhhcnNz8fLyom/fvjz11FPcfffdDq1biC6lKLD7I/jqAWgoB40Oxt8Hlz0I3n6urs7G6j1FLFmZQ1GVwXouNsSXxbPSmDkk1oWVCeHZNIrS3gCSnqO6upqQkBCqqqoIDg62uc9gMJCbm0tKSgq+vr4uqrD7kvdXOFRVAXyZCQdXq8fRQ+Ca5yBuuGvrasfqPUXMeyubM38Zt7TlvHDzCAk7QpzhbJ/frckYHSFE96Io8ONr8K+xasjR+cCkR+DO790y5JgtCktW5rQJOYD13JKVOZgtPfpvUiHOm3RdCSG6j/Kj8Pl9cKx5Be/4UXDN8xA10LV1nUVWbrlNd9WZFKCoykBWbjkZfXs5rzAhugkJOkIIz2cxw9YX4Lu/qLuMe/nBlEdhzD1uv8t4aU3HIed8rhNC2JKgI4TwbCU58PkCKGjeTiXlUpj1TwhPcW1ddooKsm98mr3XCSFsSdARQnimJiNsWAYbngZLI+iDYfpfYMQcj9qfKj0lnNgQX4qrDO2O09EAMSHqVHMhROfJYGQhhOc58SP8+zL44Uk15PS/HOZvg5FzPSrkAOi0GhbPSgNOz7Jq0XK8eFaarKcjxHmSoCOE8BymOlj9B3h5KpTtA/8IuO5V+NW7EBzn6urO28whsbxw8whiQmy7p2JCfGVquRAXSLquhBCe4cj3sHIhVOapxxffCDOXgn/36NKZOSSWaWkxsjKyEF1Mgo4Qwr3Vl8M3j8Cut9Xj4ASYtQJSp7m0LEfQaTUyhVyILiZdV05gsSgUHKjg4PZiCg5UYHHwwl8TJ05k0aJFHd6fnJzMihUrHFqDEBdMUWDvp+omnLveRt2E826Yv7VbhhwhhGNIi46DHdlZyob3D1FXeXrX74BQPRNuSKXv8CiX1LR9+3YCAgKsxxqNhk8++YTZs2e3e/2mTZu47LLLGDJkCLt27XJOkaJnqy6EL38HB75UjyMGwNXPQu8xrq1LCOFxpEXHgY7sLGX1v/fYhByAukojq/+9hyM7S11SV2RkJP7+/nZdW1lZyZw5c5gyZYqDqxICsFjgx1fVVpwDX4LWCy59AO7ZICFHCHFeJOg4iMWisOH9Q2e9ZuMHhxzWjdXU1MSCBQsICQkhIiKCRx99lJb9W1t3XSUnJwNw7bXXotForMct7rnnHm666SYyMjIcUqcQVicPwetXwhf3g7Fa3b7h7g0w+Y/gpXd1dUIIDyVBx0GKDlW2ack5U22FkaJDlQ55/TfeeAMvLy+ysrJ45plnWLZsGS+//HKb67Zv3w7Aa6+9RlFRkfW45dzRo0dZvHixQ2oUAoAmE/zwd3hhHORvBu8AmPkU3P4NRKe5ujohhIeTMToOUld99pDT2es6KzExkeXLl6PRaBgwYAC7d+9m+fLl3HnnnTbXRUZGAhAaGkpMTIz1/KFDh3jooYfYsGEDXl7yYyIc5Ph2WHkflOaox/2mwlXLIbS3a+sSQnQb0qLjIAHB9jW123tdZ40dOxZNqxViMzIyOHToEGaz+ZyPNZvN3HTTTSxZsoT+/fs7pD7RwxlrYNUD8Mo0NeT4R8AvXoFffyQhRwjRpeRPdQeJTQ0lIFR/1u6rwDA9samhzivKTjU1Nfz444/s3LmTBQsWAGCxWFAUBS8vL7755hsmT57s4iqFx9q/Clb9DqoL1OOhN8GMv3abhf+EEO5Fgo6DaLUaJtyQyup/7+nwmkuuT0XroFVPt23bZnO8detWUlNT0el0ba719va2aekJDg5m9+7dNtf861//4rvvvuOjjz4iJcUzdoUWbqa6CL56APZ9rh6HJavdVH0lNAshHEeCjgP1HR7FzLuHtFlHJzBMzyXXO3Ydnfz8fDIzM7n77rvJzs7m2Wef5emnn2732uTkZNauXcv48ePR6/WEhYUxZMgQm2uioqLw9fVtc16Ic7JYYMdrsOZxdTaVRgfj7oXLHgQf+5Y5EEKI8yVBx8H6Do8iZWikOgur2khAsNpd5aiWnBZz5syhoaGB9PR0dDodCxcu5K677mr32qeffprMzExeeukl4uPjOXbsmENrEz1ISY66P9WJLPU4bgRc/U+Iuci1dQkhegyN0rK4Sg9VXV1NSEgIVVVVBAcH29xnMBjIzc0lJSUFX1/fDp5BnC95f7uxxgb44W+w+Z9gaQKfQJj8CKTfBdq23adCCNFZZ/v8bk1adIQQXevId/BFJlTkqscDroQr/gYhCU4tw2xRZCdwIYQEHSFEF6kpga//AHs+Uo+D4uCKv8Ogq5xeyuo9RSxZmUNRlcF6LjbEl8Wz0pg5JNbp9QghXEfW0RFCXBjr/lSj1ZCj0cKYe2D+NpeFnHlvZduEHIDiKgPz3spm9Z4ip9ckhHAdadERQpy/4j3q3lQtg41jh8GsFRA33CXlmC0KS1bm0N7AQwXQAEtW5jAtLUa6sYToISTo2KGHj9d2GHlfPZixFtYtha0vgGJuHmz8KKTf6dLBxlm55W1aclpTgKIqA1m55WT07eW8woQQLiNB5yxaFtczmUz4+fm5uJrup76+HlAXLBQeQlFg/xfw1YOnVzZOuwZmLIWQeNfWBpTWdBxyzuc6IYTnk6BzFl5eXvj7+1NWVoa3tzdarQxp6gqKolBfX09paSmhoaHtrtYs3FB5rhpwDn2tHocmwZVPQ+o019bVSlSQfcsU2HudEMLzSdA5C41GQ2xsLLm5ueTl5bm6nG7nzB3ThZtqMsKmf8KGf0CTAbTeMH4hTPg/t1vZOD0lnNgQX4qrDO2O09EAMSHqVHMhRM/gdkHn+eef5+9//zvFxcUMHTqUZ599lvT09Havfemll3jzzTfZs0fdT2rkyJE88cQTHV5/Pnx8fEhNTcVkMnXZcwq1u0pacjzAke/VDThPHVaPUy6FK56GSPfc1V6n1bB4Vhrz3spGAzZhp2Xo8eJZaTIQWYgexK2Czvvvv09mZiYvvvgiY8aMYcWKFcyYMYMDBw4QFdV2X6h169bxq1/9inHjxuHr68tTTz3F9OnT2bt3L/HxXTdeQKvVysq9omepKoBv/gh7P1GPA6NhxhMw5Begce+QMHNILC/cPKLNOjoxso6OED2SW20BMWbMGEaPHs1zzz0HgMViITExkXvvvZeHHnronI83m82EhYXx3HPPMWfOHLte094lpIXoEZpMsPVf6vYNjXXqmjij74TJfwTfEFdX1ymyMrIQ3ZvHbQFhMpnYsWMHDz/8sPWcVqtl6tSpbNmyxa7nqK+vp7GxkfDwjvvfjUYjRuPpncSrq6vPv2ghupOj62DV7+HkQfU4cSxc+Q+P3YBTp9XIFHIhhPusjHzy5EnMZjPR0dE256OjoykuLrbrOR588EHi4uKYOnVqh9csXbqUkJAQ6y0xMfGC6hbC41Uehw/mwpvXqCEnIBJmvwC3fuWxIUcIIVq4TdC5UE8++STvvfcen3zyyVnH0zz88MNUVVVZb8ePH3dilUK4kSYjrP8HPJ8OOZ+q3VTpd8GCH2HYTSDLKQghugG36bqKiIhAp9NRUlJic76kpOScU5D/8Y9/8OSTT7JmzRouvvjis16r1+vR6/UXXK8QHu3g17D6ISg/qh73HqduwBkzxLV1CSFEF3ObP9l8fHwYOXIka9eutZ6zWCysXbuWjIyMDh/3t7/9jT//+c+sXr2aUaNGOaNUITzXycPw1nXwzvVqyAmMgf/3Mty6SkKOEKJbcpsWHYDMzEzmzp3LqFGjSE9PZ8WKFdTV1XHrrbcCMGfOHOLj41m6dCkATz31FI899hjvvPMOycnJ1rE8gYGBBAYGuuzfIYTbMdaoM6m2vgCWRnXRv4zfwqW/B32Qq6sTQgiHcaugc8MNN1BWVsZjjz1GcXExw4YNY/Xq1dYByvn5+TbbMLzwwguYTCauu+46m+dZvHgxjz/+uDNLF8I9WSzw0zuw9k9Q29wtnDpd3Zsqop9raxNCCCdwq3V0XEHW0RHdVv5WdW+qol3qcXgfmPkk9J/h0rKEEKIreNw6OkKILlJ5HNY8Dns+Uo/1wWoX1Zh7wMvHpaUJIYSzSdARorsw1sLG5bDlOXXzTTQw4jcw+VEIbLuFihBC9AQSdITwdBYz7HoHvvvz6XE4SZfAjL9C3LAueQnZTkEI4akk6AjhyY7+oG6+WbxbPQ5Lgel/hoFXddnmm6v3FLXZIDNWNsgUQngICTpCeKKyA/DNo3Doa/VYHwKX/V5d2dir6xbEXL2niHlvZXPmjIXiKgPz3srmhZtHSNgRQrg1CTpCeJLaUli3FHa8AYoZtF4w6na47EEI6NoNLM0WhSUrc9qEHAAF0ABLVuYwLS1GurGEEG5Lgo4QnsBYC1ueh83/BFOtem7gVTB1icPWw8nKLbfprjqTAhRVGcjKLZddwoUQbkuCjhDuzNwEO/+rtuK0DDSOGwHT/wLJ4x360qU1HYec87lOCCFcQYKOEO5IUWD/F+qKxicPqufCkmHKYhh8bZcNND6bqCDfLr1OCCFcQYKOEO7m2EZ1wb8T29Vjv3B1DM6o25y64F96SjixIb4UVxnaHaejAWJC1KnmQgjhriToCOEuin5WW3AOf6see/tDxnwYdy/4hji9HJ1Ww+JZacx7KxsN2ISdlvakxbPSZCCyEMKtSdARwtVOHoLvn4C9H6vHWi8YeQtc+gAERbu0tJlDYnnh5hFt1tGJkXV0hBAeQoKOEK5SeRx+eEpd1VgxAxoY8guY9Afo1dfV1VnNHBLLtLQYWRlZCOGRJOgI4WzVRbDhach+A8wm9Vz/y2HyIxAzxLW1dUCn1cgUciGER5KgI4Sz1Japm27++ErzpptA8gSY8hgkpru2NiGE6KYk6AjhaHUn1YX+sl6Cxnr1XOIYmPRH6HOZa2sTQohuToKOEI7SXsCJGwGT/wh9pzhlLRwhhOjpJOgI0dVqS2Hzs7D95dMBJ3YYTHwI+s+UgCOEEE4kQUeIrlJVoLbg7Hj99BicuOEw8WFInS4BRwghXECCjhAXquIYbFwBu94+PYsqfqS6Dk7/GTYBx2xRZJq2EEI4kQQdIc5XSY46i2rP/5rXwQGSxsOlv4c+E9u04KzeU9Rm4b1YWXhPCCEcSoKOEJ11PAs2LIODX50+13cyTPhdhzuKr95TxLy3stvsGVVcZWDeW9m8cPMICTtCCOEAEnSEsIfFAoe+hk3PQP6W5pMaSLsGLlmkjsXpgNmisGRlTrsbYyrqs7BkZQ7T0mKkG0sIIbqYBB0hzqbJCD9/oA4yPnlQPaf1hqE3wvhFENHvnE+RlVtu0111JgUoqjKQlVsuqw8LIUQXk6AjRHvqTsGPr8L2l6C2RD2nD4ZRt8GYeyDY/m6m0pqOQ875XCeEEMJ+EnSEaK3sAGz9F/z03ukp4kFxkPFbGDEXfIM7/ZRRQb5dep0QQgj7SdARwmKBw2tg24twZO3p87HDIGMBDJ4NOu/zfvr0lHBiQ3wprjK0O05HA8SEqFPNhRBCdC0JOqLnMlTDrncg699QfrT5pAYGXgkZ86F3Rpcs8qfTalg8K415b2WjAZuw0/Lsi2elyUBkIYRwAAk6oucp2atuz/DT+9BYp57Th8CI38DoOyA8pctfcuaQWF64eUSbdXRiZB0dIYRwKAk6omdoMsK+lbD9FcjffPp8RH8YczdcfCPoAx1awswhsUxLi5GVkYUQwokk6Iju7eRhyH5d7aKqP6We0+hg0FVq603yBKfuQaXTamQKuRBCOJEEHdH9NBpg/xfq5prHNpw+HxQLI2+BEXMgOM5V1QkhhHAiCTqie1AUKNoFO9+C3R+CoUo9r9FCv2lqwEmdDjr5kRdCiJ5EfusLz1ZbqgabXe9AyZ7T54MTYPivYfhvIDTRdfUJIYRwKQk6wvM0NsCBVeqifofXnt45XKeHQbPUgJNyGWh1rq1TCCGEy0nQEZ7B3AS5P8Ce/6mzp4zVmBUNWZaBlIaPJKp/OukTZ6ELkEX3hBBCnCZBR7gviwVOZKnhZu8nUFdmvWu1/nKWGG6gqNEHSoASiP15p6xJI4QQwoYEHeFeWsLN3k8g53OoKTx9n38vSJvN6oCrmfd1XZvtFIqrDMx7K5sXbh4hYUcIIQQgQUe4A3MjHNuoTgnf/yXUFJ2+Tx8MAy6Hi34JfSZi1nix5Knv2t0zSkHdUmHJyhympcXIQnxCCCEk6AgXMVTD0e/VYHNw9enp4NAcbq5QN9PsOxm89Na7so6cstlC4UwKUFRlICu3XBbmE0IIIUFHOFH5UTj4tRpsjm0CS+Pp+/wjYOAVMHAW9LnMJty0VlrTccg5n+uEEEJ0bxJ0hOOY6tQuqcNr4fAaKD9ie3+vftB/Jgy8ChLT7ZoOHhXka9dL23udEEIIx2q0NOKt9XbZ60vQEV3H3KSuTnx0nToVPH8rmE2n79d6Qe8MNdz0nwkR/Tr9Eukp4cSG+FJcZWh3nI4GdUfw9BSZZi6EEK6UcyqH9/a/x9airay8diV6Xfst9Y4mQUecP4sZindD3ma15ebYRjBW2V4T0hv6TYF+UyHlUvANvqCX1Gk1LJ6Vxry3stGATdhpGXq8eFaaDEQWQggXMJlNfH3sa9478B4/l/1sPb/xxEamJE1xSU0SdAQAZotCVm45pTUGooLUFpE2YcFUD4U74fg2yN+ittgYq22v8Q1RdwRPuQz6TISI1C7fHXzmkFheuHkES1bm2AxMjgnxlXV0hBDCBU7UnOCjgx/xyeFPKDeUA+Cl9WJ60nR+NfBXDI0c6rLaJOgIVu8pahMaYoN9WTwxnJmBh6BghxpuineDpcn2wfpg6D0WksapLTaxw5yy9cLMIbFMS4s5dzgTQgjhEGaLmQ0FG3j/wPtsKtiE0tzGHuUfxfX9r+cX/X9BhF+Ei6sEjaIo7Q116DGqq6sJCQmhqqqK4OAL61bxRKv3FDHvrew24100zWde8F7BTN3203cExaoDhxPHQvJ4iB4ie0oJIUQPUlxXzCeHP+HjQx9TXFdsPZ8Rm8H1A65nYuJEvLSOb0ex9/NbWnQcwNRQz6ZXX6S6pIbg6CDG33YPPn7+ri4LFEVdjK90H5Ttx1yyjyXbL0EhkNMjXJovRYMGC0uUO5iWPhJd4mhIHAMhCV3eFSWEEMK9NVma2FiwkY8OfsSGgg1YFAsAIfoQru13Ldf1v46k4CQXV9k+tws6zz//PH//+98pLi5m6NChPPvss6Snp3d4/Ycffsijjz7KsWPHSE1N5amnnuKKK65wYsW2vvjznynMTaPRZ5h6ohQOzf+KuJQcrnr0UccXoChQXw6Vx6A8F04dgVOHm29HbAYLZ5kHUWS+vOOnQktRUxBZAx6QxfeEEKIHyq/O55PDn/D54c8pbSi1nh8dM5rrUq9jStIUl82mspdbBZ3333+fzMxMXnzxRcaMGcOKFSuYMWMGBw4cICoqqs31mzdv5le/+hVLly7lqquu4p133mH27NlkZ2czZMgQp9f/xZ//TN6JcXDGcgGN3qHknRjHF3/+84WFHUVRB//WlkFtMVQXQnWB+rWqACrzoCIPTDUdP4dGB+F9IGogpU0ZsPvcLyuL7wnhfhRFQVHUr1hsjxUFFIsCyhnnrde1us/S6vma72/3uNVrcMZroTSPzrCgjtNQOF1b6+tAPT7zmjPOq69v/Yeqp5RW51oewJnnzs/pRmqN+r2m5Ui9s+WcRv1/aJofoGn5XnPG91qN9VhjPT7je636vba949Zfdc3fN39tOe9I9Y31rM1fy8eHPubHkh+t58P0YVzT7xr+X+r/IyUkxaE1dCW3GqMzZswYRo8ezXPPPQeAxWIhMTGRe++9l4ceeqjN9TfccAN1dXV88cUX1nNjx45l2LBhvPjii3a9ZleN0TE11PP6/K9o9A5tv2tHUfBurOD/3Z+A3scLraUJTZMJjdmkLqzXWAemWjDWq0HFUAWGSvVrQyXUn4K6k2AxtX3u9vhHQmgihKVAWFLz12Q04Smg8wFgW34lc98/d9J548aLGJMUph60/Nva7b7SWDvAFI3G9prWj9NorL8o7PktZddPqHLmYTsPOvMXaLuvZc/jOv4F2/qX9ulzHTzO5hrF9vozfunbPpdivebMx7f3WJsPojMer3DGfa0/hGj+sIT2j1t9eFk/0Dj9wdnyvOpxq9ds82HZfH/rD9z2PiSVs93X/KHcUkvLhzKA5YwA0PIYi2K9XrGcUa9NOGgbINp82HcUAiztPwdnPObMa2j1Oqf/rbbXiR5Mgxp6WsKPTotGp0Gnaz6n0zZ/Vb/XeXX8VeelReulRafTUGYq5UD1fg5U7cegNNCkbUTRmunbqw9jEkYzNPpi9HofdN5adN5avKxfdXj5qMdandZpb4PHjdExmUzs2LGDhx9+2HpOq9UydepUtmzZ0u5jtmzZQmZmps25GTNm8Omnn3b4OkajEaPRaD2urq7u8NrO2PTqi6e7q9qj0dDoE85n//wcg25qqzu0QFDzrQuVAyfOPFkDnF7XwIJCYDDUtvoLxoYCQQr8/OIh9rR7gRDCU2i0zX+IaJtbGmjV8tDcQtBy3NKK0XINrVonrPe1HNPq8We2bsAZj4HWrSa217S6r6XmlsdaT1j/X9uWl0464+8P2z9IWv+9ckYgP7MlqnWQt7akcUZ4tfmqYLGcEYKbv7dYFBSzgqUlCFs6SLQKWMwKFnPL/ebzeAfaF0gKIzmjteYwFG6DQvae8/FarUYNPT6601+9tUy9NY2wmIAuq7Mz3CbonDx5ErPZTHR0tM356Oho9u/f3+5jiouL272+uLi43esBli5dypIlSy684DNUl5ylu6gVjaUR3GSSkhYNUxp8+MzfhHXr7xbN//1MbvBB21NCjrUpQqWh1S+ZNr9vTjfLWN+dNn9mnz62vab1eZvfqOd4zubzrZ7D+viWD4N2PlxOt6JB6w8pNM1N4BqttTkdrQaNtvlYq4WW73VaNDrd6XOtmvPV11Ob3ls+qGxe/4wPtjOb+6G52Z4z6zv3B23ra6wfvq3r0La9ps3jzzjWnnndGd0QtHQztGqZ1La8zhl1tH6MVtvynO1co231PrRTz+k629Zk87otX1s9V+tAITyLtcXPrIYgi9mCYgGz2YJiUaxhx9Lq/pZjc8v3TRbMTQoWiwVLk4K5yUKdsYF9ZfvYV7qf4poStIoOncULvcaXpIBkegckE+oVhqVJoanRgrnJjLlRoanRjLnRgrnJop5vtNBkUo9bWCwKJoMZk8E2fJ3x69Wp3CboOMvDDz9s0wpUXV1NYmLiBT9vcHQQlJ77uoQhvRg1dzAWiwWzYsaiWDArFposTdavTUoTZksTTZZGGi2NmCyNmMyNGC1GzOZGDGYDJnMjhiYjJrMRo9mAwWykwWzA2GTA0NRAQ5OBhsYGGprqaTA3UNdYT0NjPWbF9odPAwTXpVF78ios5hDrea1XJUG9vmRrQA5bz/g3+On8CNYHE6oPIcw7lGDfEEJ9QgjxDSXMJ4QQfSjh+lBC9WGE+Ybir/OzfYLTfx5Zv29pnrf5MLfeB2iajy0W2+uV5mOzBU2raxSzBeufXhb1lwIWs7qaskUBxYJiNkPzecViAYvG9pzZ0nydpfnYrD5Xk/n0/eYmFLMFpakRzM3PaW5CaWxSn7Ple7NZvabJjNLU1HxrPm5sPH2usVE939j8fct9Ld+bTO0EKufT+Pqi9fND6+eHxt8PrX8AWn//treAgNNfAwLQBgZYv9cFBqJtuendezCjEK5gDcpaTfPfx+f/V7LRbGT9ifV8efRL1hetp9HSCKGgCdUwJnYMs/rOYmrvqfh7d36GsGJRaGqyYDZZaGo009T6q0n9Ghjuuv/G3SboREREoNPpKCkpsTlfUlJCTExMu4+JiYnp1PUAer0evQN+qY6/7R4O2TFGZ+I997lsqrmiKBjNRmoba6k11VLXWEe1qZraxlqqDbXsPlFFWY0RjVc1/sFF1JpCqTYNocpYpd5MVVgUC/XUcspSCw2F0ACco/fPV+dLL79e9PLtRS+/XkT6RRLhF2H9PtI/kki/SHr59XLK2gueTmlqQjGZ1Ftz+LGYTCimRhSTUT1vNKrnjM3XmYxYDIbm4+bvDcbm740ohoYzvhqwGAxYDA0oDQYsDQ0ohtOD0hWDAbPBgLmiokv+TRpv79OhJygQXWAQ2qAgNQwFB6MLCkIbHIQuKNj6VRcSjC44GG1wMNrAQGm1EOIMTZYmsoqz+Cr3K9bkraG2sdZ6X7/QfszqO4srUq4gJqDjz0x7aLQavH10ePvoaDMbxw24zaeKj48PI0eOZO3atcyePRtQByOvXbuWBQsWtPuYjIwM1q5dy6JFi6znvv32WzIyMpxQsS0fP3/iUnLUWVeKYht2mv8Cj0vZh4/fdU6vrYVGo8HXyxdfL992V6v8xYCzP96iWKhtrKXKUEWFsYJKYyUVBvVruaGcCkMFFYYKyg3lnDKcotxQTkNTAwazgYLaAgpqC85eHxrCfcOJ8o+y3iL9I4nxjyHaP5rogGii/aMJ9Am8kLfB42m8vNB4eYG/cwOzYrGoAaihQb3V16NYv1ePLfV16te65q+tv6+tU7+vq8NSW4u5thalvl597sZGzBUV5x+cdDp0QUHoQkLQhoSga7mFhp7+PiwUXWiYei4sDK+wUDT+/hKQRLditpjJLs1mde5q1uSvsW7HABATEMMVKVdwRcoV9A/r32N+9t1q1tX777/P3Llz+fe//016ejorVqzggw8+YP/+/URHRzNnzhzi4+NZunQpoE4vv+yyy3jyySe58soree+993jiiSc6Nb28q1dGPr2OTpj1nLepnLiUfc5ZR8fN1DfWc8pwilMNzTfDKU42nORkw0nKGso4Wd/8teFkm261jgR4BxDtH01MQIx681e/xgbGEhsQS0xAjNuv6yBUitncHIJq1fBTU4ultgZzTQ2WmlrMNdVYWs5V16jHVdWYa2owV1dhqapWu/POk8bHB11YWPMtFK+wcHTh4ejCw/AKV7/36tXL+lUbFNRjPhyE52gJN9/mfcuavDWUNZRZ7wvThzE1aSpX9rmS4VHD0WqcNyvK0ez9/HaroAPw3HPPWRcMHDZsGP/85z8ZM2YMABMnTiQ5OZnXX3/dev2HH37II488Yl0w8G9/+1unFgx0xBYQbrsyshuzKBbKDeWU1ZdR1lBGSX0JZfVllNaXUlxfTGl9KSV1JVSb7JslF+4bTmxALHGBcdavcQFx6tfAOIJ8uniWm3AZi8GAuboaS1UV5qoqzNXVmCsq1a+VlZirKjFXVqnfV1aqLUeVlSitZl/aS+Ptja5XLzX8RPTCq1cEXhHNt0j1qy4iAq/ISLQBARKKhMM0WhrZUbKDNXlrWJO3hlOGU9b7gnyCmNJ7CpcnX87o2NF4a92vO6kreGzQcbaevteVp6lvrKekvoTiumL1Vl9MSV0JRXVFFNcVU1RXRENTwzmfJ8gniPjAeOIC4ogPiic+MJ6EwAT1XGDceQ3IE57FUl+PuaKCporm8FNRTlN5OebyCprKT2Eur8B86hRNFepXS11dp55f4+eHV2Tk6VtUJN5RUXjZ3KLRBbpmyq3wPIYmA5sLN7M2fy3rjq+z+cMvyCeIyYmTmZ48nbGxY/FpXi+tO5OgYycJOt2LoihUm6oprC2kqK6Ioroi6/cFtQUU1RZRYTz3OJBevr2ID1LDT0JQgvVrYlAiUf5R3ar5V9jHYjBgLi+n6dQpmk6eVEPQSfX7plMnMZedVL8/eRJLbe25n7CZNiAAr+hovKKj8I6OwSsmGu/oaLyiY/COicYrNhZdaKi0DvVQ5YZy1p9Yz/f537OlaIvNH3Jh+jAmJk5kevJ0xsSMwVvXPVtuOiJBx04SdHqe+sZ6CmsLKawrVAdJ1xRYB0ufqDlBTePZ10Ty0foQFxhHYlBim1t8ULyMDxJYGhpoKis7fSsto6mslKbSUhpLS9XjkhK7A5HG11cNP7GxeLfc4mJtjrV+fud+IuH2FEXhSOUR1hesZ93xdewq3WWz0ntsQCxTek9hcu/JDI8a3qNnqkrQsZMEHXGmKmOVTfApqC3geM1xTtScoLC2kCalqcPHatAQHRBNYlAivYN6q1+De1u/ly4x0Zqlro7GklKaSktoKilRvy8uprGkRP1aXIz51KlzPxGgCw/HOy7u9C0+3uYmXWTuq6Gpge3F21l/Yj0bTmygsK7Q5v5B4YOYmDiRiYkTGRQ+SFr3mknQsZMEHdEZTZYmSupLOF5z3Ho7UXPC+n1d49nHcUT4RdA7qPfp8BOcSFJQEr2DexPgLR9Eoi2LyaSGoKIiNfwUFdNYWEhjUSFNRUU0FhRiaZ6mfza6kBC8ExKab/H4JCbinZCIT2IC3rGxaHy6/5gOd6EoCrlVuWws2Mimwk38WPwjplb7GPpofUiPTefShEuZlDjpgte56a4k6NhJgo7oKoqiUGGsIL863xp88mvyOV6tfq00Vp718eG+4SQFJ5EYlEhSsBp+koKSSApOkpYg0SFFUbBUV6vhp7CQxoJCGgsKmr8voLGgAHNV1dmfRKvFOyYG78REvBMT8EnsjU/vRLybv+rkd+MFKzeUs61oG1sKt7ClaAvFdbZbFcUExDAhfgKXJlxKeky6/DdvBwk6dpKgI5ylyljFiZoT5Nfkk1edZw1E+TX5Not6taelJaglALV8L91hwh7m2lo19Jw4QeOJE5hOFNB4/DiNBScwHT9hs+p1e3RhYXj3TsSndxI+vXvjk5yET5L6vS401Dn/CA9T11jHjpIdbC/ezraibewr32dzv4/Wh1ExoxgfN55L4i8hJSRFuqQ6SYKOnSToCHdQY6ohvyaf/OrmW6swdK5ZYlF+UWrrT6tWoN7B6pggXy9fJ/0LhKdSFAXzqVOY8o/TeOK4+vV4Pqb845jy8885RkgXEoJPcrIafpKTT9+SktA6efVuV6prrGNX6S52lOwgqziLPSf3tFkEtX9YfzJiM8iIy2BE9Aj8vGQA+YWQoGMnCTrC3VWbqsmvPh188mryOF59nLyaPKqMZ++SiPaPbhOAWsYGyewwYQ9zbd3p4JOXhyk/j8ZjeZjy82kqPftOxl4xMdYQpO/TB5+UFHxS+uAdF4tG69lLNFQYKthVuovs0mx+LP6RfeX72gSb+MB4xsSOYXTMaMbGjm136x1x/iTo2EmCjvBkVcYq8qrz1BDU3Ap0vPo4edV5Z50m3zI7rPXA6JYAlBiUKH9pCrtY6usx5edjOpaH6dgxNQjl5mLKyzvrvmUavV4NQH1S0Kf0wadvH2sQ0vq6XyukRbFwrPoYP5X+xK6yXews3UluVW6b6+ID4xkZPZJR0aNIj00nPjDeBdX2HBJ07CRBR3RHiqJQaay0CUAtXWL51fk2uxi3J8ovisTg01PkW24JQQmE6EOc9K8QnsxcWYkxN1cNQbm5mHJzMeYepTEvH6Wxsf0HaTR4x8c3B5++6Pv2wadvX/T9+qELct62LRWGCvac3MPuk7v5uexnfj75MzWmtn849Anpw/Co4dZwExsY67QahQQdu0nQET3NmbPDbFqCavLa/YXeWrBPcJvwkxiUSEJgAlH+Uei0Oif9S4QnUpqaaCwoUEPQkaMYc49iOpqL6ciRs84O84qORt+3Lz79+qLv2w99aj81AF3g7+0qYxX7y/eTcyqHPSf3sPfUXgpqC9pc56vzJa1XGsOihjE8ajjDIocR6ht6Qa8tLowEHTtJ0BHCVpWxyhqC8mrybNYJOtlw8qyP9dJ6WfcNSwhS9w6LD4y3fh/sEywzS0S7FEXBXF6O8cgRTEePYjxyFNORwxiPHKWppKTDx3lFRqJPTW2+9UOfmopP335tFkhUFIXiumIOVBzgQPkBDlQcIOdUTruhBiA5OJnBEYMZGjmUiyMvpn9Y/267OaankqBjJwk6QtivvrHeukjiidoTNosmnmvVaIBA70DrxqktX+MC4ogNjJUgJDpkrqnBdOQIxsOHMR5u/nrkCE1FRR0+xhITSW1iGEVR3hwKM/JjYCkHgusw69r+fCUEJjCo1yDSeqUxJGIIab3SCPaRzwN3J0HHThJ0hOgaZouZkvoSawhq2T7jRO0JCmoKOGU491YG/l7+xAbEEhsYS2xALHGBcUT7RxMTEKPe/GN63MaFon0WxUJxyVFO7NlK+b5dmA4dwTuviNDCGkJrLO0+pkkLpyL11PeOwKtfX3pdNJLk4ZcSnjxAArYHkqBjJwk6QjhHQ1MDRbXqLvKFteqGqoV1heoGq7WFdgUhDRp6+fUi2j9avQWoX6P8o6y3aP9oWUSxm2iyNFFUV2TTgtiy1MLxmuMYzcZ2HxdYrzCsNpyLq0PpU+5FZFE9fvllUNv+VhnaoCD0/fvjO6A/+v4D0A/oj2///mgDZFsWdyZBx04SdIRwD4YmA0V1RRTVFVFcV0xhbaH1+5Zb6/2AzibAO4BIv0gi/SOJ8ItQv/eLpJdfL3r59SLCL4IIvwhCfEJk8LQL1TXWUVJXQnFdMSX1JTbBt+V/+zPXpmnNS+tFQmACKSEp9AnpQ5/QPqQEp5ASkkKgT6DNtYqi0FRcjPHgQQwHD2I8eAjjgQMYc3Ohg1lg3r174ztgAPoBA/AdOAD9wIF4x8dL64+bkKBjJwk6QniGltlixXXFlNaXUlJXQkm9+iFZ2lBKab16O9fGqq1pNVrC9GGE+4UT7htOuD6cMN8wQn1DCdeHE+obSqhevYXoQwjRh+Cr85UPurMwW8xUGCsoN5RzquGU9evJhpOUNZRRVl9GWUMZpfWl51zmANStEhKC1MHtCYEJ1lXAk4KSiA2MxUvrdUH1KiYTxtxjGA/sV0PQgYMYDxzocDFEbWAg+oED8B0wUP06cBD61H5uuf5PdydBx04SdIToXuoa6yitL1U/WJs/VMvqyzhpOMnJhpOcajjFqYZT59xaoyPeWm+CfYIJ1gerX32CCfIJIsgniEDvQAJ9Agn0DiTAOwB/b3/8vfzV77388fP2w89LvbljYFIUBZPFRENjAw1N6q22sZbaxlrqG+upbaylxlRDtamaamO1+tVUTaWxkkpDJZXGSqpN1Z16zSCfIGtXZGxgLHEBceog9cA4YgNiifKPQqtx/irKTRUVGA8cwLB/P8b9BzAcOIDx8OH2W390OvR9UtAPGIjvoIHoBw7Ed9AgvMLDnV53TyJBx04SdITomRotjVQaKtUWB4MafiqNlVQYKqgwVqhfDRVUGauoNFZSZaw656yyztLr9Oh1enx1vvjofNDr9HjrvPHWNt903nhpvPDSeqHT6NBpdeg0OjRo0Gg0aDVaNKhhSWn+P1AH6loUC2aLGbNixqJYaLI00WhppNHSSJOlCZPZhNFstLk1NDVgUdofyNsZGjSE6kPp5ddLbSnzDSfSX+0+jPCLIMo/iki/SKIDognw9pxxMEpjI8ajuRj378Ow/wDGA/sx7Nvf4SrQXtHR+A4ciH7QQHwHpeGbNgjvhAS3C7ieSoKOnSToCCHsoSgKdY11VJmq1FYNY7W1daPGVGNt7ahtrKXWVEt9Uz11jXXUNdZR31hPfVM9hiYDBvPZdwp3Fz5aH/y8/aytU4Hegfh7+xPkHWRtzQryCSLYJ1jt3jujm+9Cu5Q8haIoNJWWqeFn334M+/Zh3L8fU15eu9drg4LwHTgQ37RB6AcNwndQGvo+KWi8ZTZhZ0nQsZMEHSGEM1kUC4YmAw1NDRjNRgxmAyazCUOTgUZLIyazydryYjKbMCtmzBYzTUqTtYVGURQsigUF9WtLC08LrUartgBpdGi16vfeWm+8tF7W1iIvrZfamuTle7plycvX2rXWU4KKo5hr6zAePIBh3z41/OTsw3joULvbX2h8fNRZX4MG4Ts4Dd+0NPT9+8u4n3OQoGMnCTpCCCGcQe36OoohZx+GfTkYc/Zh2L8fS207g7J1OvR9+uCbNgjftObwM2gQusDAttf2UBJ07CRBRwghhKsoFguNx4+rLT85+zDk5GDYtw/zqfbXlfJO6m0NPi03r7AwJ1ftHiTo2EmCjhBCCHeijvspVUNPTo61BaipsP0tL7zj4qxdXtbwExnp5KqdT4KOnSToCCGE8ARNFRWtwo96a8zLb/dar8hINfQMHmwNQV4xMd1qxpcEHTtJ0BFCCOGpzDU1zd1ezeFnbw6m3FywtF0mQBcebtvtNTjNo6e7S9CxkwQdIYQQ3Ymlvh7DgQMY9raEn70YjxyBprbrQGmDg9uM+fFJTkKjdf4ijZ0lQcdOEnSEEEJ0dxajUd3iYq8afAw5ORgPHmx3urvW3x99q9levmlp6Pv0QePlXksOSNCxkwQdIYQQPZFiMmE8csTa6mPYm4PhwAEUQ9tFLTV6vbq3V+vwk5qK1sfHBZWrJOjYSYKOEEIIoVKamjDl5tKwdy/GffvU8LNvH5a6djbL9fZG36+futbPoOYANHAAWn9/p9QqQcdOEnSEEEKIjikWC435+afDT04ODXtzsFRVtb1Yo8EnJUVd5TktrTkEDUIXGtrldUnQsZMEHSGEEKJzFEWhsaAQwz51wLOxebHDprKydq/v/frrBIwd06U12Pv57V4ji4QQQgjh9jQaDT4J8fgkxBM8bZr1fFNZ2elVnpv3+WrMz0ffP9VltUrQEUIIIUSX8IqMJDAyksBLL7WeM9fWunSPLvefKC+EEEIIj+XqjUgl6AghhBCi25KgI4QQQohuS4KOEEIIIbotCTpCCCGE6LYk6AghhBCi25KgI4QQQohuS4KOEEIIIbotCTpCCCGE6LYk6AghhBCi25KgI4QQQohuS4KOEEIIIbotCTpCCCGE6LYk6AghhBCi2/JydQGupigKANXV1S6uRAghhBD2avncbvkc70iPDzo1NTUAJCYmurgSIYQQQnRWTU0NISEhHd6vUc4Vhbo5i8VCYWEhQUFBaDSaLnve6upqEhMTOX78OMHBwV32vMKWvM/OI++1c8j77BzyPjuHI99nRVGoqakhLi4OrbbjkTg9vkVHq9WSkJDgsOcPDg6W/4icQN5n55H32jnkfXYOeZ+dw1Hv89laclrIYGQhhBBCdFsSdIQQQgjRbUnQcRC9Xs/ixYvR6/WuLqVbk/fZeeS9dg55n51D3mfncIf3uccPRhZCCCFE9yUtOkIIIYTotiToCCGEEKLbkqAjhBBCiG5Lgo4QQgghui0JOhfg+eefJzk5GV9fX8aMGUNWVtZZr//www8ZOHAgvr6+XHTRRaxatcpJlXq2zrzPL730EhMmTCAsLIywsDCmTp16zv9dxGmd/Zlu8d5776HRaJg9e7ZjC+wmOvs+V1ZWMn/+fGJjY9Hr9fTv319+f9ihs+/zihUrGDBgAH5+fiQmJnL//fdjMBicVK1nWr9+PbNmzSIuLg6NRsOnn356zsesW7eOESNGoNfr6devH6+//rpji1TEeXnvvfcUHx8f5dVXX1X27t2r3HnnnUpoaKhSUlLS7vWbNm1SdDqd8re//U3JyclRHnnkEcXb21vZvXu3kyv3LJ19n2+66Sbl+eefV3bu3Kns27dPueWWW5SQkBDlxIkTTq7c83T2vW6Rm5urxMfHKxMmTFCuueYa5xTrwTr7PhuNRmXUqFHKFVdcoWzcuFHJzc1V1q1bp+zatcvJlXuWzr7Pb7/9tqLX65W3335byc3NVb7++mslNjZWuf/++51cuWdZtWqV8sc//lH5+OOPFUD55JNPznr90aNHFX9/fyUzM1PJyclRnn32WUWn0ymrV692WI0SdM5Tenq6Mn/+fOux2WxW4uLilKVLl7Z7/fXXX69ceeWVNufGjBmj3H333Q6t09N19n0+U1NTkxIUFKS88cYbjiqx2zif97qpqUkZN26c8vLLLytz586VoGOHzr7PL7zwgtKnTx/FZDI5q8RuobPv8/z585XJkyfbnMvMzFTGjx/v0Dq7E3uCzgMPPKAMHjzY5twNN9ygzJgxw2F1SdfVeTCZTOzYsYOpU6daz2m1WqZOncqWLVvafcyWLVtsrgeYMWNGh9eL83ufz1RfX09jYyPh4eGOKrNbON/3+k9/+hNRUVHcfvvtzijT453P+/z555+TkZHB/PnziY6OZsiQITzxxBOYzWZnle1xzud9HjduHDt27LB2bx09epRVq1ZxxRVXOKXmnsIVn4U9flPP83Hy5EnM/7+9+wtpqg/jAP6dzuPEVlHiZrUEB9EKS7Ayy5Cgm4iCCgqK4U2YSTdRkTRigVuMkm6iCEPyIlAsjCCjf6ugFOmiHZKyhUoF2YTAaFSga8975XjNBW1vO3vP8fuBc+Hv/A57zsPR8+XnOeznT9hstmnjNpsNb968SXpMJBJJOj8SiWSsTr1Lp8+/OnHiBBYtWjTjF4umS6fXz549Q1tbG1RV1aBCY0inzyMjI3j06BH279+PO3fuYGhoCI2NjZicnITX69WibN1Jp8/79u3D58+fUVNTAxFBLBZDQ0MDTp48qUXJs8bv7oVfv37Fjx8/UFBQ8Nc/kys6ZFiBQACdnZ24efMmLBZLtssxlGg0CrfbjStXrqCoqCjb5RhaPB5HcXExWltbUVlZib1798Lj8eDy5cvZLs1Qnjx5gjNnzuDSpUt48eIFuru70dPTg+bm5myXRv8RV3TSUFRUhNzcXIyNjU0bHxsbg91uT3qM3W5PaT6l1+cpLS0tCAQCePjwIVatWpXJMg0h1V4PDw/j3bt32L59e2IsHo8DAMxmM8LhMJxOZ2aL1qF0rumSkhLk5eUhNzc3MeZyuRCJRDAxMQFFUTJasx6l0+dTp07B7XbjwIEDAIDy8nJ8+/YN9fX18Hg8yMnhusDf8Lt74dy5czOymgNwRSctiqKgsrISwWAwMRaPxxEMBlFdXZ30mOrq6mnzAeDBgwe/nU/p9RkAzp49i+bmZty9exdr1qzRolTdS7XXy5cvx8DAAFRVTWw7duzA5s2boaoqHA6HluXrRjrX9MaNGzE0NJQIkgDw9u1blJSUMOT8Rjp9/v79+4wwMxUuhV8J+ddk5V6YscecDa6zs1Py8/Olvb1dXr9+LfX19TJ//nyJRCIiIuJ2u6WpqSkxv7e3V8xms7S0tMjg4KB4vV6+Xv4HUu1zIBAQRVHkxo0b8unTp8QWjUazdQq6kWqvf8W3rv5Mqn3+8OGDWK1WOXz4sITDYbl9+7YUFxeLz+fL1inoQqp99nq9YrVapaOjQ0ZGRuT+/fvidDplz5492ToFXYhGoxIKhSQUCgkAOX/+vIRCIXn//r2IiDQ1NYnb7U7Mn3q9/Pjx4zI4OCgXL17k6+X/ZxcuXJClS5eKoiiybt066e/vT+yrra2Vurq6afO7urpk2bJloiiKrFy5Unp6ejSuWJ9S6XNpaakAmLF5vV7tC9ehVK/pf2PQ+XOp9rmvr0+qqqokPz9fysrKxO/3SywW07hq/Umlz5OTk3L69GlxOp1isVjE4XBIY2OjjI+Pa1+4jjx+/Djp39yp3tbV1Ultbe2MYyoqKkRRFCkrK5OrV69mtEaTCNfkiIiIyJj4jA4REREZFoMOERERGRaDDhERERkWgw4REREZFoMOERERGRaDDhERERkWgw4REREZFoMOERERGRaDDhERERkWgw4RzSpHjhzBrl27sl0GEWmEQYeIZpXnz5/zW+2JZhEGHSLSvUOHDqGmpibpviVLliAQCGBiYgJ5eXno6+uDx+OByWTC+vXrNa6UiLRmznYBRET/xatXr9Da2oqnT58m3e9yuaCqKsxmM3p7e1FVVQVVVWGz2WCxWDSuloi0xqBDRLp27tw5rF27Fhs2bEi6f8GCBYhEIsjJycHo6CgWLlyI1atXa1wlEWUL/3VFRLoVi8XQ3d2N3bt3J8YOHjyItra2xM/RaBQFBQUAgFAoxJBDNMsw6BCRbg0PDyMajaK8vBwAEI/Hcf36dVit1sScly9fYsWKFQAAVVUZdIhmGQYdItKtL1++AADmzJkDALh37x7Gx8cTz9709/fj48eP2LlzJwBgYGAAFRUV2SiViLKEz+gQkW6VlpbCZDKho6MDhYWFOHbsGLZt24Zbt27B4XCgoaEBW7ZsSbyRFY/HEQ6HMTo6isLCQsybNy/LZ0BEmcYVHSLSLbvdDr/fj2vXrmHr1q04evQo/H4/gsEgNm3aBJfLha6ursR8n8+H9vZ2LF68GD6fL4uVE5FWTCIi2S6CiIiIKBO4okNERESGxaBDREREhsWgQ0RERIbFoENERESGxaBDREREhsWgQ0RERIbFoENERESGxaBDREREhsWgQ0RERIbFoENERESGxaBDREREhvUPhub6BDcri4YAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6721067",
+   "id": "abfba6fc",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "da2a127f",
+   "id": "d519e0ca",
    "metadata": {},
    "source": [
     "### おまけ: スピン総和\n",
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a8d9bcfd",
+   "id": "a8937b09",
    "metadata": {},
    "source": [
     "## 問題2: シュウィンガーモデル\n",
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45a67a21",
+   "id": "be477d2e",
    "metadata": {},
    "source": [
     "### シュウィンガーモデルの物理\n",
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b904a60a",
+   "id": "36697bc6",
    "metadata": {},
    "source": [
     "### ハミルトニアンを物質場のみで記述する\n",
@@ -296,7 +296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5f2f45d",
+   "id": "2828b5f6",
    "metadata": {},
    "source": [
     "### ハミルトニアンをパウリ行列で表現する\n",
@@ -337,7 +337,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "060db42c",
+   "id": "9b1a5b77",
    "metadata": {},
    "source": [
     "### 問題\n",
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "06b7711b",
+   "id": "3bda474e",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -442,7 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "42fc61fd",
+   "id": "828ec02f",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -453,7 +453,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f30ecab1450>]"
+       "[<matplotlib.lines.Line2D at 0x7f89977f57b0>]"
       ]
      },
      "execution_count": 4,
@@ -462,7 +462,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ef10d27",
+   "id": "970c8a4a",
    "metadata": {
     "tags": [
      "remove-output"
diff --git a/_sources/numerics.ipynb b/_sources/numerics.ipynb
index b5e9bf4..9a616fd 100644
--- a/_sources/numerics.ipynb
+++ b/_sources/numerics.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "35be1713",
+   "id": "fe1e0743",
    "metadata": {},
    "source": [
     "# 予備知識：数値表現\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7102b1f2",
+   "id": "6f68353b",
    "metadata": {},
    "source": [
     "(signed_binary)=\n",
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52e78091",
+   "id": "c559115d",
    "metadata": {},
    "source": [
     "(nonintegral_fourier)=\n",
@@ -61,13 +61,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "05ff171b",
+   "id": "6df522ad",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f41a0345690>"
+       "<matplotlib.legend.Legend at 0x7f924c331540>"
       ]
      },
      "execution_count": 1,
diff --git a/_sources/prerequisites.ipynb b/_sources/prerequisites.ipynb
index 34df126..1cdc3a5 100644
--- a/_sources/prerequisites.ipynb
+++ b/_sources/prerequisites.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "726dcbe9",
+   "id": "6bfe718e",
    "metadata": {},
    "source": [
     "# 実習の準備\n",
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "454731b7",
+   "id": "cad85884",
    "metadata": {},
    "source": [
     "## IBM Quantum\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9fe8dad6",
+   "id": "d4c2a93a",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5e0e50bb",
+   "id": "e8707f34",
    "metadata": {},
    "source": [
     "トークンを保存することで、プログラム中でのIBM Quantumへの認証（IBMProviderの取得）は\n",
diff --git a/_sources/python_intro.ipynb b/_sources/python_intro.ipynb
index b85ea20..059d9e3 100644
--- a/_sources/python_intro.ipynb
+++ b/_sources/python_intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c7f86719",
+   "id": "1e98a2a6",
    "metadata": {},
    "source": [
     "# 予備知識：Python\n",
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61a98cf0",
+   "id": "c8290701",
    "metadata": {},
    "source": [
     "## 基礎編\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "66c97250",
+   "id": "6d1850e9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "cfc3e578",
+   "id": "0dedc325",
    "metadata": {
     "tags": [
      "remove-output"
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "a0face02",
+   "id": "40094cb6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06b046f6",
+   "id": "85138cd2",
    "metadata": {},
    "source": [
     "あとで解説する関数やクラスの定義、`if`、`for`、`try`/`except`などの複合文では、全ての行を一定数の空白文字分（通常スペース4つ、流儀によっては2つ。タブ文字は使うべきでない）だけインデントさせます。"
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "39c8f2f2",
+   "id": "30f6f087",
    "metadata": {
     "tags": [
      "remove-output"
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9e6c990",
+   "id": "1ad6b08f",
    "metadata": {},
    "source": [
     "インデントしたブロックの中で、さらにインデントを必要とする文を書く場合は、スペース8つ、12個、と入れ子状にインデントします。各レベルでのインデンテーション解消がブロックの終わりを意味します。"
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "db37c606",
+   "id": "47bddd62",
    "metadata": {
     "tags": [
      "remove-output"
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f99a386",
+   "id": "a10dd1f4",
    "metadata": {},
    "source": [
     "```{admonition} インデンテーション\n",
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "00d0bcf1",
+   "id": "5d843896",
    "metadata": {},
    "source": [
     "### 変数\n",
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "aea348a5",
+   "id": "4456b405",
    "metadata": {
     "tags": [
      "remove-output"
@@ -233,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "f6891bf7",
+   "id": "89105a27",
    "metadata": {
     "tags": [
      "remove-output"
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d308c33b",
+   "id": "f8939c17",
    "metadata": {},
    "source": [
     "```{admonition} 文字列\n",
@@ -262,7 +262,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "62d5e2d2",
+   "id": "1de22e4c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3af6426",
+   "id": "d3c1f3c4",
    "metadata": {},
    "source": [
     "### 演算子\n",
@@ -289,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "6b5e1065",
+   "id": "9853fef8",
    "metadata": {
     "tags": [
      "remove-output"
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4bbb124e",
+   "id": "1c1e37e4",
    "metadata": {},
    "source": [
     "他にもビットシフト演算子などが存在しますが、このワークブックでは使用しないので割愛します。\n",
@@ -385,7 +385,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "54d48dc1",
+   "id": "82929509",
    "metadata": {
     "tags": [
      "remove-output"
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cae71ec6",
+   "id": "d49691f7",
    "metadata": {},
    "source": [
     "### 基本のデータ型\n",
@@ -433,7 +433,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1c02dc15",
+   "id": "8ca05bc6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8f01a68",
+   "id": "f2509508",
    "metadata": {},
    "source": [
     "#### 文字列型 str\n",
@@ -482,7 +482,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "1cc74e66",
+   "id": "d8c785d1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6d26455",
+   "id": "2c3073ec",
    "metadata": {},
    "source": [
     "同じことを例えばdouble quotesだけで表現することもできます。その場合、文字列中のクオーテーション記号にバックスラッシュを付け、文字列を囲む記号と区別します。"
@@ -512,7 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "450f56a6",
+   "id": "07e20c8c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b9bf834",
+   "id": "8a3847ab",
    "metadata": {},
    "source": [
     "上に比べて少しコードが読みにくくなることがわかります。\n",
@@ -544,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "0f580b50",
+   "id": "227ec16d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -566,7 +566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b6104ce",
+   "id": "c5fa5eae",
    "metadata": {},
    "source": [
     "これもまた読みにくいので、実はsingle quotesとdouble quotes以外にそれらの記号を3つ並べた`'''`や`\"\"\"`（三重クオート）でテキストを囲み、改行を含む文字列をそのまま書くこともできます。"
@@ -575,7 +575,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c895bc63",
+   "id": "cb23e20f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b79522d",
+   "id": "b20b9785",
    "metadata": {},
    "source": [
     "改行やクオーテーション以外にも「特殊文字」があり、全てバックスラッシュで始まります。実際、通常の文字列中でバックスラッシュが一つ登場すると、Pythonはそのバックスラッシュと次の文字（場合によっては複数字）を組み合わせて特殊文字として解釈します。なので、実際のバックスラッシュをテキスト中に登場させたい場合（LaTeXのテキストを書いているときなど）は、一つだけ書くと解釈されないので、`\\\\`という特殊文字を利用します。"
@@ -616,7 +616,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "a0c22b38",
+   "id": "3a528ddc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -637,7 +637,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a42be7bc",
+   "id": "c9614d50",
    "metadata": {},
    "source": [
     "これも読みにくいテキストになってしまうので、バックスラッシュがたくさん使われる文字列を書く場合は、`r`というプレフィックスをクオーテーションの前にくっつけます。`r`のついた文字列中では、バックスラッシュが特殊文字として解釈されません。"
@@ -646,7 +646,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "1f072807",
+   "id": "4c2f010d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f40ac8a",
+   "id": "d84a6554",
    "metadata": {},
    "source": [
     "ときどき、文字列型以外の変数を文字列に変換する必要が出てきます。例えば、`\"The value of variable x is \"`という文字列の後に、`x`という変数の値を付け加えたいとします。`x`が整数型だと`+`演算子を使ったらエラーになります。"
@@ -676,7 +676,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "de32c089",
+   "id": "d177c2a1",
    "metadata": {
     "tags": [
      "remove-output",
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69d488a0",
+   "id": "32c27f50",
    "metadata": {},
    "source": [
     "文字列と整数の足し算は定義されていないためです。このような場合は、`str(x)`として`x`の10進数表現の文字列を作ります。"
@@ -715,7 +715,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "817e4ab7",
+   "id": "01416b13",
    "metadata": {
     "tags": [
      "remove-output"
@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0b4221bb",
+   "id": "7321fa63",
    "metadata": {},
    "source": [
     "ただ、この方法で変数の文字列表現をたくさん作ると、下のようにコードが冗長になりやすい問題があります。"
@@ -746,7 +746,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "e98e9469",
+   "id": "dad1eaa6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -767,7 +767,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9048400",
+   "id": "fc959d70",
    "metadata": {},
    "source": [
     "そこで、Pythonでは文字列中に変数のプレースホルダーを入れ、後から変数値を代入することができるようになっています。ただ、歴史的経緯から、4つの異なる方法が実装されていて、少し厄介です。このワークブックでは2通りのみ使用することにします。\n",
@@ -778,7 +778,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "87065d38",
+   "id": "1e12eea4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -799,7 +799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f869a91",
+   "id": "0a4dcb8c",
    "metadata": {},
    "source": [
     "f-stringは、実は変数に限らず、その場で評価可能な任意のプログラムコードを扱えます。中括弧の中にさらに文字列リテラルが入る場合は、全体の文字列に使っていないクオーテーション記号を使います。"
@@ -808,7 +808,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "11d7cb12",
+   "id": "90e97e55",
    "metadata": {
     "tags": [
      "remove-output"
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1339a7f",
+   "id": "7fae26a1",
    "metadata": {},
    "source": [
     "このようにf-stringで大体の目的は達成できますが、厄介になるのは実際に中括弧を含む文字列を作りたい場合です。f-string中の中括弧は二重にして表現するので、簡単な文字列でもコードが非常に読みにくくなります。"
@@ -842,7 +842,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "bb07c252",
+   "id": "c209aec7",
    "metadata": {
     "tags": [
      "remove-output"
@@ -867,7 +867,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "88196faa",
+   "id": "f48bdbda",
    "metadata": {},
    "source": [
     "そこで、このワークブックでは中括弧を含む文字列に変数値を代入するときに限り、最も古い方式である%-formattingを使います。この方式でのプレースホルダーは、整数値を代入したいときは`%d`、実数値なら`%f`、文字列なら`%s`など、`%`文字と代入したいデータの型の識別子の組み合わせでできます。変数値を代入するには、文字列のすぐ後に`　%　変数`をくっつけます。\n",
@@ -880,7 +880,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "53076209",
+   "id": "fc5e8921",
    "metadata": {
     "tags": [
      "remove-output"
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d9bdbf67",
+   "id": "5c9f880f",
    "metadata": {},
    "source": [
     "#### 配列型 tuple, list\n",
@@ -912,7 +912,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "848be509",
+   "id": "5c6555b9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -938,7 +938,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d2ae3800",
+   "id": "a9b8200f",
    "metadata": {},
    "source": [
     "タプルやリストの要素を一つ参照するときは、鉤括弧でインデックス（要素の番号）を指定します。インデックスは0から始まるので、長さnの配列に対して有効なインデックスは0からn-1までです。"
@@ -947,7 +947,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "4130a97a",
+   "id": "98562b7a",
    "metadata": {
     "tags": [
      "remove-output"
@@ -973,7 +973,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "73e6ef06",
+   "id": "e66bfccd",
    "metadata": {},
    "source": [
     "リストでは要素を新しい値にすることができます。"
@@ -982,7 +982,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "3162bc51",
+   "id": "25641534",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1006,7 +1006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17730a28",
+   "id": "734bbe50",
    "metadata": {},
    "source": [
     "あとで説明するfor文を使って、配列の要素を順番に参照することもできます。"
@@ -1015,7 +1015,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "fc12b47d",
+   "id": "108007e8",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1040,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "810ed282",
+   "id": "86716090",
    "metadata": {},
    "source": [
     "タプルやリストの長さ（要素数）は`len()`関数でわかります。"
@@ -1049,7 +1049,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "4c5d2658",
+   "id": "7ca81543",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c01d4649",
+   "id": "991ed5bb",
    "metadata": {},
    "source": [
     "タプル同士やリスト同士は足し算で繋ぎ合わせられます。"
@@ -1081,7 +1081,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "86e79cda",
+   "id": "ec0fbf66",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c17b0cc1",
+   "id": "fcfdc6e6",
    "metadata": {},
    "source": [
     "数値に対してと同様、足し算での更新も可能です。"
@@ -1120,7 +1120,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "ff374153",
+   "id": "d526c1a6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1145,7 +1145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3f745934",
+   "id": "33a1f910",
    "metadata": {},
    "source": [
     "リストの最後に新たな要素を足すには、`append()`というメソッド（後の解説を参照）を使います。"
@@ -1154,7 +1154,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "99136695",
+   "id": "86272a6c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1176,7 +1176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a0834e5",
+   "id": "7a654e39",
    "metadata": {},
    "source": [
     "すでに説明した通り、タプルには要素を足せないので、対応するメソッドがありません。"
@@ -1184,7 +1184,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "037e0cdf",
+   "id": "88044bb4",
    "metadata": {},
    "source": [
     "#### 辞書型 dict\n",
@@ -1195,7 +1195,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "951524f7",
+   "id": "52972772",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1213,7 +1213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0ffd09a",
+   "id": "950bffb5",
    "metadata": {},
    "source": [
     "辞書型の要素を参照するときは鉤括弧にキーを入れます。辞書型と同様、要素の値を入れ替えることができます。また、全く新しいキーを加えることもできます。"
@@ -1222,7 +1222,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "f3c69734",
+   "id": "3e0da11c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1247,7 +1247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54549dc9",
+   "id": "094c43b2",
    "metadata": {},
    "source": [
     "辞書型に対しても`len()`が使えます。"
@@ -1256,7 +1256,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "e0c87a4c",
+   "id": "242d0b9c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1277,7 +1277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71f67117",
+   "id": "91dc7e11",
    "metadata": {},
    "source": [
     "#### 真理値型 bool\n",
@@ -1288,7 +1288,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "9ce10c99",
+   "id": "a32f50c3",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1340,7 +1340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1beb57b2",
+   "id": "031d3aa6",
    "metadata": {},
    "source": [
     "#### None型\n",
@@ -1350,7 +1350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "401bb38c",
+   "id": "ef22b1d2",
    "metadata": {},
    "source": [
     "### 関数\n",
@@ -1369,7 +1369,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "c344ef55",
+   "id": "d792e2d4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1400,7 +1400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28d1d24d",
+   "id": "63b2096c",
    "metadata": {},
    "source": [
     "関数の引数にはデフォルトの値を持たせることができます。そのような引数には、関数実行時に値を指定されなければ、設定されたデフォルト値が代入されます。\n",
@@ -1415,7 +1415,7 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "id": "6b0e3c95",
+   "id": "94b9c57e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1455,7 +1455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e0e060d7",
+   "id": "b25598c9",
    "metadata": {},
    "source": [
     "### if文（条件文）\n",
@@ -1466,7 +1466,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "d8018895",
+   "id": "512101b3",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1494,7 +1494,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "6de33a5f",
+   "id": "d9d2deb5",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1517,7 +1517,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "b39aa517",
+   "id": "f1b0788e",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -1544,7 +1544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6171784e",
+   "id": "a191e54e",
    "metadata": {},
    "source": [
     "```{admonition} 変数のスコープ\n",
@@ -1557,7 +1557,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "cdd29889",
+   "id": "71d9a52e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1584,7 +1584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "964e6751",
+   "id": "615e5136",
    "metadata": {},
    "source": [
     "### for文、while文（ループ）\n",
@@ -1595,7 +1595,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "0502c961",
+   "id": "40918cdb",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1646,7 +1646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2af28228",
+   "id": "c10f3bff",
    "metadata": {},
    "source": [
     "### クラス、オブジェクト、メソッド\n",
@@ -1659,7 +1659,7 @@
   {
    "cell_type": "code",
    "execution_count": 44,
-   "id": "45138045",
+   "id": "53f62552",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b01ab4bb",
+   "id": "ccf41438",
    "metadata": {},
    "source": [
     "このままでは`EmptyClass`というクラスがプログラムの中で定義されているという他、何の役割も果たしません。プログラムによってはあるクラスが存在するだけでいいということもありますが、通常は属性（インスタンスに付随するデータ）を足したり、「メソッド」を定義したりします。メソッドというのはクラスに対して定義される関数のことです。インスタンスを自動的に引数に取るため、挙動がインスタンスごとに異なります。"
@@ -1682,7 +1682,7 @@
   {
    "cell_type": "code",
    "execution_count": 45,
-   "id": "b39689f0",
+   "id": "f3468660",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1723,7 +1723,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4b5606bb",
+   "id": "b8966e8e",
    "metadata": {},
    "source": [
     "### モジュールのインポート\n",
@@ -1734,7 +1734,7 @@
   {
    "cell_type": "code",
    "execution_count": 46,
-   "id": "5df3f9e5",
+   "id": "da5232d6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1764,7 +1764,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1a8430a",
+   "id": "b2598ee6",
    "metadata": {},
    "source": [
     "Pythonの非常に大きな強みが、ユーザーベースの広さとオープンソースのライブラリの多さです。Pythonでやりたいと思うことは大体誰かがすでに実装していて、[PyPI](https://pypi.org)などでパッケージとして公開しているので、ユーザーはパッケージをインストールしてプログラム中からモジュールを`import`すればいい、という具合です。"
@@ -1772,7 +1772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "858a05fb",
+   "id": "531ac630",
    "metadata": {},
    "source": [
     "### 例外（exception）\n",
@@ -1783,7 +1783,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "036303e0",
+   "id": "1b525ab7",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1816,7 +1816,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d1ce5b42",
+   "id": "f0569eff",
    "metadata": {},
    "source": [
     "上の例にあるように、例外にも型があり、例外発生のさまざまな理由を区別できるようになっています。例外とその処理はPythonの正常なプログラムフローの一部として捉えるべきで、`try``except`を上手に使うことでより「Pythonらしい」コードが書けると言われています。\n",
@@ -1827,7 +1827,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "23248662",
+   "id": "d06ccfec",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1855,7 +1855,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca43bd56",
+   "id": "c10c0c8f",
    "metadata": {},
    "source": [
     "## 実用編\n",
@@ -1870,7 +1870,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "a12eefef",
+   "id": "f2d78f43",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1930,7 +1930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8df699df",
+   "id": "89ad8b57",
    "metadata": {},
    "source": [
     "文字列（`str`）、リスト（`list`）、タプル（`tuple`）、辞書（`dict`）などの組み込み型でよく使うメソッドも紹介しておきます。"
@@ -1939,7 +1939,7 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "id": "425923e8",
+   "id": "f57a8a8e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1991,7 +1991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6544f624",
+   "id": "54b80d45",
    "metadata": {},
    "source": [
     "リスト、タプル、辞書などを生成するときに時々使う構文を紹介します。"
@@ -2000,7 +2000,7 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "id": "f0ebd796",
+   "id": "35b12d4e",
    "metadata": {},
    "outputs": [
     {
@@ -2027,7 +2027,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "066c8f6a",
+   "id": "b52b14f0",
    "metadata": {},
    "source": [
     "### NumPy\n",
@@ -2040,7 +2040,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "505c4b1e",
+   "id": "230e64db",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2053,7 +2053,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e19ec9e0",
+   "id": "073d654c",
    "metadata": {},
    "source": [
     "#### 配列を作る\n",
@@ -2066,7 +2066,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "32a0ff56",
+   "id": "4b6e17d1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2097,7 +2097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5b3c303",
+   "id": "46054a88",
    "metadata": {},
    "source": [
     "#### 配列の要素を参照する"
@@ -2106,7 +2106,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "ebc5fcce",
+   "id": "0f3e862d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2144,7 +2144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a3e26d9f",
+   "id": "55f002d3",
    "metadata": {},
    "source": [
     "#### 配列を操作する"
@@ -2153,7 +2153,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "9fd2324c",
+   "id": "74c58fdc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2187,7 +2187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1914d1ce",
+   "id": "c42f4d78",
    "metadata": {},
    "source": [
     "#### 配列に対して計算を施す\n",
@@ -2198,7 +2198,7 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "id": "928f93e9",
+   "id": "3c43e716",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2229,7 +2229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0661887",
+   "id": "7b129bc3",
    "metadata": {},
    "source": [
     "のようになります。これだと簡単なことをするために何行もコードがいるだけでなく、実は非常に非効率です。というのも、Pythonには、コード一行一行の実行が比較的遅いという弱点があるからです。これはPythonがCやJavaのようにプログラム全体を「コンパイル」して最初から機械言語に変換して実行する言語ではないことに起因します。上のようにループを回すと、ループの繰り返しごと、一行ごとに書かれている命令を解釈し直すので、時間がかかります。無論繰り返し数3のループではCなどとPythonの間で人間が感知できるような時間の差はありませんが、配列の大きさが例えば数十万を超えてくると、明らかに違いが見えてきます。\n",
@@ -2240,7 +2240,7 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "id": "7fdc8510",
+   "id": "6d27b495",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2266,7 +2266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "575c9db3",
+   "id": "a138d67e",
    "metadata": {},
    "source": [
     "となります。コードの行数が少なくなっただけでなく、実はこの場合はCなどと同じ速さで計算が行われます。NumPyはPythonから扱えるモジュールですが、中身はコンパイルされて極度に最適化されたライブラリなのです。\n",
@@ -2277,7 +2277,7 @@
   {
    "cell_type": "code",
    "execution_count": 58,
-   "id": "564d3445",
+   "id": "c8ad0fb7",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "478c0f8d",
+   "id": "b2f51c07",
    "metadata": {},
    "source": [
     "要素ごとではなく配列全体にかかる関数もあります。"
@@ -2337,7 +2337,7 @@
   {
    "cell_type": "code",
    "execution_count": 59,
-   "id": "c80de031",
+   "id": "da8e506e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2365,7 +2365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18870d6c",
+   "id": "8a6fa138",
    "metadata": {},
    "source": [
     "NumPyで実装されている関数はあまりにも多いので、このワークブックで使われている分でもここで紹介しきれていないかもしれません。残りは[NumPyのドキュメンテーション](https://numpy.org/doc/stable/)を参照してください。"
@@ -2373,7 +2373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fcfc2103",
+   "id": "647ea3de",
    "metadata": {},
    "source": [
     "### Matplotlib\n",
@@ -2386,7 +2386,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "cffeee81",
+   "id": "ae05b1bf",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2400,7 +2400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6bd411b1",
+   "id": "a0d1f6c9",
    "metadata": {},
    "source": [
     "Matplotlibには「図」（Figure）と「グラフ描画領域」（Axes）という概念があります。一つのFigureの中に複数のAxesが存在でき、各Axesに一つのプロットが描画される構造になっています。\n",
@@ -2411,7 +2411,7 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "id": "4eedda86",
+   "id": "4f01df1e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2451,7 +2451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af360290",
+   "id": "840e42c4",
    "metadata": {},
    "source": [
     "こうして作ったFigureやAxesオブジェクトの様々なメソッドを使ってグラフを描くことができますが、実は単にxとyのデータがあって一つスキャッタープロットを作りたいという時などは、もっと簡単な方法を取ることもできます。"
@@ -2460,7 +2460,7 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "id": "273e6f27",
+   "id": "028cf4b4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2470,7 +2470,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f581bb75870>]"
+       "[<matplotlib.lines.Line2D at 0x7f983bdedba0>]"
       ]
      },
      "execution_count": 62,
@@ -2499,7 +2499,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "c5e9a449",
+   "id": "7192dca6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2509,7 +2509,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f5844c066b0>"
+       "<matplotlib.legend.Legend at 0x7f983bc190c0>"
       ]
      },
      "execution_count": 63,
@@ -2538,7 +2538,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67d6049c",
+   "id": "0e169eec",
    "metadata": {},
    "source": [
     "`plt.plot`や`plt.scatter`を複数実行すると、同じAxesにグラフが重なってプロットされます。実行の際に`label='some text'`という引数を足し、`plt.legend()`を呼ぶと、凡例が表示されます。また`plt.xlabel()`や`plt.ylabel()`でX, Y軸にタイトルをつけたり、`plt.title()`でプロット全体にタイトルをつけたりできます。"
@@ -2546,7 +2546,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89436c5a",
+   "id": "baeacf93",
    "metadata": {},
    "source": [
     "### Jupyter\n",
@@ -2559,14 +2559,14 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "224fb4c3",
+   "id": "767a9c23",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using matplotlib backend: <object object at 0x7f581d0b2ac0>\n"
+      "Using matplotlib backend: <object object at 0x7f983f096ac0>\n"
      ]
     }
    ],
@@ -2588,7 +2588,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "dc35109a",
+   "id": "6916f721",
    "metadata": {},
    "outputs": [
     {
@@ -2610,7 +2610,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "d523ef82",
+   "id": "ed75d027",
    "metadata": {},
    "outputs": [
     {
diff --git a/_sources/qkc_machine_learning.ipynb b/_sources/qkc_machine_learning.ipynb
index e4714c3..dfa4071 100644
--- a/_sources/qkc_machine_learning.ipynb
+++ b/_sources/qkc_machine_learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bf2f4c47",
+   "id": "9cc55a01",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64d313c5",
+   "id": "3b2bc6a4",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee06c337",
+   "id": "714a4dcc",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f655b526",
+   "id": "bff27ce1",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d77701e",
+   "id": "6dfe047e",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d241428",
+   "id": "64725740",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4003521f",
+   "id": "8358f1d5",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc988423",
+   "id": "2b842ed7",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "75fcece6",
+   "id": "43b4ba0a",
    "metadata": {},
    "outputs": [
     {
@@ -306,7 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "e1c8d66a",
+   "id": "34b92b5c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c426574",
+   "id": "8f187e46",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e072a5db",
+   "id": "c2a828bd",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "594e2938",
+   "id": "72366bcc",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -453,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8cdc2bb8",
+   "id": "12abd659",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01090407",
+   "id": "a15995b4",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "cae6f547",
+   "id": "a9c9a3f3",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "227813bc",
+   "id": "d3723f19",
    "metadata": {},
    "source": [
     "次に同じことを`FidelityQuantumKernel`クラスを利用して行います。"
@@ -542,7 +542,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cfb92882",
+   "id": "78ce7655",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -583,7 +583,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f463f29f",
+   "id": "30d6ede8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -600,7 +600,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "515b2055",
+   "id": "97be9f23",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b7e6d8",
+   "id": "cc6a0639",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "f8f2d362",
+   "id": "8b278c68",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3eefb65",
+   "id": "62bb97fa",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/quantum_computation.ipynb b/_sources/quantum_computation.ipynb
index 2747af0..5f06fbe 100644
--- a/_sources/quantum_computation.ipynb
+++ b/_sources/quantum_computation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "6d1154bf",
+   "id": "2898ed50",
    "metadata": {},
    "source": [
     "# 【課題】アダマールテスト\n",
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d2c9e3f0",
+   "id": "33c1929e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ef5790e",
+   "id": "4689e432",
    "metadata": {},
    "source": [
     "## 問題1: アダマールテストで状態ベクトルを同定する\n",
@@ -123,7 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "461e4f62",
+   "id": "34581b2e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a1d42e0",
+   "id": "f0d90d66",
    "metadata": {},
    "source": [
     "Qiskitでは、`QuantumCircuit`オブジェクトで表される量子回路を、`to_gate()`メソッドで一つのゲートオブジェクトに変換することができます。さらにそのゲートに対して`control(n)`メソッドを用いると、元の回路をn量子ビットで制御する制御ゲートを作ることができます。"
@@ -156,7 +156,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "7eed4b9e",
+   "id": "e6ed4b76",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bc653980",
+   "id": "93352059",
    "metadata": {},
    "source": [
     "$U^{-1}_k$とその制御ゲート化は$k$の関数として定義しておきます。"
@@ -175,7 +175,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "166f649b",
+   "id": "fdd95e15",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4237e7a8",
+   "id": "58b5674a",
    "metadata": {},
    "source": [
     "ゲートオブジェクトは`QuantumCircuit`オブジェクトに`append()`で組み込むことができます。制御ゲートを埋め込む場合は、制御ビットが組み込み先の最初のnビットとなるので、`append()`の`qargs`引数で正しく対応づけします。\n",
@@ -211,7 +211,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "0ce3f38c",
+   "id": "3c40d6ba",
    "metadata": {
     "tags": [
      "remove-output"
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c7d0428b",
+   "id": "8ac30cc4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68e4c80f",
+   "id": "abb2b09b",
    "metadata": {},
    "source": [
     "得られた結果と状態ベクトルシミュレータで計算される状態ベクトルとを比較してみましょう。"
@@ -309,7 +309,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "7997185a",
+   "id": "7a3715c2",
    "metadata": {
     "tags": [
      "remove-output"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86ab4a39",
+   "id": "609538a0",
    "metadata": {},
    "source": [
     "## 問題2: 符号が反転している基底を見つける\n",
@@ -378,7 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "757a65d9",
+   "id": "59f9b553",
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "085a4567",
+   "id": "5058c10c",
    "metadata": {},
    "source": [
     "ここまでは{doc}`circuit_from_scratch`の問題5と同じです。\n",
@@ -428,7 +428,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "bd87fb68",
+   "id": "d61a8fb7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c01727a",
+   "id": "8b71ff74",
    "metadata": {},
    "source": [
     "それでは、この制御ブラックボックスゲートを利用して、equal superpositionにある`haystack`レジスタで干渉を起こして、観測で`needle`が識別できるような回路を書いてください。\n",
@@ -451,7 +451,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "61705426",
+   "id": "151b6dbb",
    "metadata": {
     "tags": [
      "remove-output"
@@ -484,7 +484,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "30d78ba5",
+   "id": "0e140828",
    "metadata": {
     "tags": [
      "remove-output"
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "400e5aae",
+   "id": "e5c33920",
    "metadata": {},
    "source": [
     "回路が完成したら、`qasm_simulator`で実行し、ヒストグラムをプロットしてください。"
@@ -519,7 +519,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "3897251b",
+   "id": "3f6258dc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -528,7 +528,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 1600x400 with 1 Axes>"
       ]
@@ -548,7 +548,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee470e39",
+   "id": "3c1642ea",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/_sources/shor.ipynb b/_sources/shor.ipynb
index 4584f76..ba48f9f 100644
--- a/_sources/shor.ipynb
+++ b/_sources/shor.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e0f9bf27",
+   "id": "13f667e4",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce45ed02",
+   "id": "95813908",
    "metadata": {},
    "source": [
     "この実習では**ショアのアルゴリズム**を学習します。名前を聞いたことがある人もいるかもしれませんが、ショアのアルゴリズム{cite}`shor,nielsen_chuang_qft_app`は最も有名な量子アルゴリズムと言っても良いでしょう。ショアのアルゴリズムの元になっている**量子位相推定**と呼ばれる手法を学んだ後、ショアのアルゴリズムの各ステップを実例とともに紹介します。最後に、Qiskitを使用してショアのアルゴリズムを実装し、実際に素因数分解を行ってみます。\n",
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "55a2dda7",
+   "id": "55ca3bc9",
    "metadata": {},
    "source": [
     "(qpe)=\n",
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f40916b",
+   "id": "bc873959",
    "metadata": {},
    "source": [
     "(qpe_1qubit)=\n",
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5bfffbd2",
+   "id": "9da27423",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f9b637c",
+   "id": "0578541f",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -155,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "dde3dcba",
+   "id": "940fecff",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6f6b617",
+   "id": "57dfc57d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -194,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "52045d40",
+   "id": "b608b282",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f40d919",
+   "id": "3cdde042",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -243,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "928426f1",
+   "id": "ac5d3e71",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aab6e6f",
+   "id": "a2144982",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -332,7 +332,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "cf99fb16",
+   "id": "58be7b50",
    "metadata": {
     "tags": [
      "remove-input",
@@ -343,7 +343,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f35a7eb2ce0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f798bc268f0>"
       ]
      },
      "execution_count": 4,
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4423cac2",
+   "id": "c6ef2e4e",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -406,7 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "a36f0771",
+   "id": "ec0bf4a5",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dab81c0c",
+   "id": "775b3dd8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e291ee1",
+   "id": "7c837382",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "eb74b722",
+   "id": "58e98372",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -545,7 +545,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "bcba9b77",
+   "id": "1e0c0051",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -578,7 +578,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "d4df0610",
+   "id": "8c7d3ed9",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9eea57c9",
+   "id": "7637582f",
    "metadata": {},
    "source": [
     "(shor_algo)=\n",
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f899effb",
+   "id": "284df93c",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1cb86b88",
+   "id": "ef3f5461",
    "metadata": {},
    "source": [
     "(shor_circuit)=\n",
@@ -733,7 +733,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b138566",
+   "id": "970bb1f9",
    "metadata": {},
    "source": [
     "(shor_measurement)=\n",
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e09b8af9",
+   "id": "dff17944",
    "metadata": {},
    "source": [
     "(continued_fractions)=\n",
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cdd569d2",
+   "id": "bbb2bb21",
    "metadata": {},
    "source": [
     "(modular_exponentiation)=\n",
@@ -843,7 +843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45440cbf",
+   "id": "71b72452",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -863,7 +863,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "c9312e7b",
+   "id": "08e7c1b1",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -909,7 +909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2dc11917",
+   "id": "1e745609",
    "metadata": {},
    "source": [
     "(shor_imp_oracle)=\n",
@@ -927,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "c5b13e50",
+   "id": "6a0456b4",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -974,7 +974,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "45063039",
+   "id": "20b2dfe4",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1106,7 +1106,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2c5355fa",
+   "id": "d3b55470",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1151,7 +1151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4c7dc89",
+   "id": "9ac75467",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1167,7 +1167,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "200a64ba",
+   "id": "960b1922",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1225,7 +1225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a07b002",
+   "id": "d4c98398",
    "metadata": {},
    "source": [
     "シミュレータで実行して、結果を確認してみます。"
@@ -1234,7 +1234,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "e1691df3",
+   "id": "518a2b8b",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1246,7 +1246,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1265,7 +1265,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87b87d5b",
+   "id": "72012cd1",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1280,7 +1280,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "b6c03808",
+   "id": "c1767abb",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1297,9 +1297,9 @@
       "Register Output    Phase\n",
       "------------------------\n",
       "128      128/256 = 0.500\n",
-      "192      192/256 = 0.750\n",
       " 64       64/256 = 0.250\n",
-      "  0        0/256 = 0.000\n"
+      "  0        0/256 = 0.000\n",
+      "192      192/256 = 0.750\n"
      ]
     }
    ],
@@ -1322,7 +1322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18913cd2",
+   "id": "a3325fe0",
    "metadata": {},
    "source": [
     "得られた位相の情報から、連分数アルゴリズムを使用して$s$と$r$を見つけることができます。Pythonの組み込みの`fractions`(分数)モジュールを使用して、小数を`Fraction`オブジェクトに変換できます。"
@@ -1331,7 +1331,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "eb91479b",
+   "id": "30118fce",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1348,9 +1348,9 @@
       "     Phase   Fraction   Guess for r\n",
       "-------------------------------------\n",
       "     0.500       1/ 2             2\n",
-      "     0.750       3/ 4             4\n",
       "     0.250       1/ 4             4\n",
-      "     0.000       0/ 1             1\n"
+      "     0.000       0/ 1             1\n",
+      "     0.750       3/ 4             4\n"
      ]
     }
    ],
@@ -1370,7 +1370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26b57ff8",
+   "id": "88a07776",
    "metadata": {},
    "source": [
     "`limit_denominator`メソッドを使って、分母が特定の値（ここでは15）を下回る分数で、最も位相の値に近いものを得ています。\n",
diff --git a/_sources/spectrum_estimation.ipynb b/_sources/spectrum_estimation.ipynb
index 3229cf7..65c8790 100644
--- a/_sources/spectrum_estimation.ipynb
+++ b/_sources/spectrum_estimation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0f44d5f0",
+   "id": "d8a0154c",
    "metadata": {},
    "source": [
     "# 【課題】位相推定によるスペクトル分解\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d37afc7f",
+   "id": "2e2c696b",
    "metadata": {},
    "source": [
     "## エネルギースペクトルを求める\n",
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df554724",
+   "id": "82f59461",
    "metadata": {},
    "source": [
     "## ハイゼンベルグモデル再考\n",
@@ -59,7 +59,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "67076c47",
+   "id": "2071019a",
    "metadata": {
     "tags": [
      "remove-output"
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d01033b2",
+   "id": "0ae6b97e",
    "metadata": {},
    "outputs": [
     {
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22bce2da",
+   "id": "dbeb006a",
    "metadata": {},
    "source": [
     "最後の部分で、[`show_state`関数](https://github.com/UTokyo-ICEPP/qc-workbook/tree/master/source/qc_workbook/show_state.py)を利用して固有値と固有ベクトルを表示しました。最低エネルギー状態（固有値$-2J$）に対応する独立な固有ベクトルが3つあることがわかります。したがって、これらの固有ベクトルの任意の線形和もまた最低エネルギー状態です。励起状態（固有値$6J$）は$1/\\sqrt{2} (-\\ket{01} + \\ket{10})$です。"
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c9e5ffe",
+   "id": "2c71623e",
    "metadata": {},
    "source": [
     "## 位相推定によるスペクトル分解\n",
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9b0e77fb",
+   "id": "ee1901e7",
    "metadata": {},
    "source": [
     "## 問題1: スペクトル推定を実装し、厳密解と照らし合わせる\n",
@@ -293,7 +293,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1244d62e",
+   "id": "f6380883",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e6e0e972",
+   "id": "4427b7df",
    "metadata": {},
    "source": [
     "次のセルでスペクトル推定のアルゴリズムを実装しています。この関数は状態レジスタ、読み出しレジスタ、時間発展回路を引数に取り、スペクトル推定の量子回路を返します。"
@@ -362,7 +362,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "6678030f",
+   "id": "a1a412d1",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f5f3468",
+   "id": "a79e373a",
    "metadata": {},
    "source": [
     "この問題では、上で厳密解を求めた$n=2, g=0$のケースを調べます。今回はすでにエネルギー固有値を知っているので、ハミルトニアンの規格化定数を$\\hbar \\omega = 16J$として、読み出しレジスタの終状態が単純になるようにします。このとき読み出しは符号付きで、最大絶対値が$2^{n_R} (6/16)$なので、$n_R = 1 + 3$とすればオーバーフローを回避できます。\n",
@@ -429,7 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "74b974b3",
+   "id": "86141946",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80970ce0",
+   "id": "6a64f501",
    "metadata": {},
    "source": [
     "上で正しく関数をかけているか確認しておきましょう。"
@@ -462,7 +462,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "2783e184",
+   "id": "f647e2b1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -489,7 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "83938e34",
+   "id": "00b8ca6e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cd01a5b",
+   "id": "6991128b",
    "metadata": {},
    "source": [
     "状態レジスタの初期状態を\n",
@@ -531,7 +531,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "19dd9cb1",
+   "id": "0e5a1ff6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c1d94910",
+   "id": "ad984bfa",
    "metadata": {},
    "source": [
     "最後に全てを組み合わせます。"
@@ -583,7 +583,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "adfe2394",
+   "id": "42b0e151",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1608b7a1",
+   "id": "3977c4aa",
    "metadata": {},
    "source": [
     "シミュレータで実行してヒストグラムを得ます。"
@@ -629,7 +629,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "3cab897c",
+   "id": "0ee3db72",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "477421a1",
+   "id": "68ff6e6e",
    "metadata": {},
    "source": [
     "状態レジスタの初期状態が式{eq}`two_qubit_init`なので、回路の終状態は\n",
@@ -686,7 +686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d498cbe3",
+   "id": "b590fbc8",
    "metadata": {},
    "source": [
     "## 問題2: 非自明な系の振る舞いを調べる\n",
@@ -749,7 +749,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "50394f16",
+   "id": "effbe088",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,7 +828,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59e89731",
+   "id": "b3d93dfb",
    "metadata": {},
    "source": [
     "読み出しレジスタのビット数を決めます。スピンの数が4なので、$\\hbar \\omega = 8(3 + |g|)J$と取ります。すると、$g=0$のとき、上の議論によると$\\Theta$の固有値の予想される最小絶対値は$1/24$ですが、実は系の対称性からその$n=4$倍の$1/6$が最小値になると予測できます。$|g| \\ll 1$しか考えないので、外部磁場を摂動として考えて、結局$2^{n_R} / 6$が1より十分大きくなるように$n_R=5$とします。\n",
@@ -839,7 +839,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "4978e551",
+   "id": "9e27d41e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -852,33 +852,27 @@
      "text": [
       "Computed spectrum for g = 0.0 l = 0\n",
       "Computed spectrum for g = 0.0 l = 1\n",
-      "Computed spectrum for g = 0.0 l = 2\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computed spectrum for g = 0.0 l = 2\n",
       "Computed spectrum for g = 0.0 l = 3\n",
-      "Computed spectrum for g = 0.0 l = 4\n",
-      "Computed spectrum for g = 0.0 l = 5\n",
-      "Computed spectrum for g = 0.0 l = 6\n"
+      "Computed spectrum for g = 0.0 l = 4\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.0 l = 5\n",
+      "Computed spectrum for g = 0.0 l = 6\n",
       "Computed spectrum for g = 0.0 l = 7\n",
       "Computed spectrum for g = 0.0 l = 8\n",
-      "Computed spectrum for g = 0.0 l = 9\n",
-      "Computed spectrum for g = 0.0 l = 10\n"
+      "Computed spectrum for g = 0.0 l = 9\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.0 l = 10\n",
       "Computed spectrum for g = 0.0 l = 11\n",
       "Computed spectrum for g = 0.0 l = 12\n",
       "Computed spectrum for g = 0.0 l = 13\n",
@@ -928,7 +922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "89904c9d",
+   "id": "f6c653d9",
    "metadata": {},
    "source": [
     "得られた$P(k)$分布を、$k$をエネルギーに換算してプロットしてみましょう。"
@@ -937,7 +931,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "db4bcff2",
+   "id": "508da197",
    "metadata": {
     "tags": [
      "remove-output"
@@ -973,7 +967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f26e1f6b",
+   "id": "d13b0c7e",
    "metadata": {},
    "source": [
     "続いて、同じ関数を$g=0.1, 0.2, 0.3, 0.4, 0.5$について実行して、それぞれのスペクトルから系のエネルギー固有値と$g$の関係をプロットしてください。"
@@ -982,7 +976,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "63780aa9",
+   "id": "72bf0c50",
    "metadata": {
     "tags": [
      "remove-output"
@@ -993,17 +987,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.1 l = 0\n",
-      "Computed spectrum for g = 0.1 l = 1\n",
-      "Computed spectrum for g = 0.1 l = 2\n"
+      "Computed spectrum for g = 0.1 l = 0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.1 l = 1\n",
+      "Computed spectrum for g = 0.1 l = 2\n",
       "Computed spectrum for g = 0.1 l = 3\n",
-      "Computed spectrum for g = 0.1 l = 4\n",
+      "Computed spectrum for g = 0.1 l = 4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Computed spectrum for g = 0.1 l = 5\n"
      ]
     },
@@ -1021,39 +1021,57 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.1 l = 10\n",
+      "Computed spectrum for g = 0.1 l = 10\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Computed spectrum for g = 0.1 l = 11\n",
       "Computed spectrum for g = 0.1 l = 12\n",
-      "Computed spectrum for g = 0.1 l = 13\n"
+      "Computed spectrum for g = 0.1 l = 13\n",
+      "Computed spectrum for g = 0.1 l = 14\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.1 l = 14\n",
-      "Computed spectrum for g = 0.1 l = 15\n",
-      "Computed spectrum for g = 0.2 l = 0\n",
-      "Computed spectrum for g = 0.2 l = 1\n"
+      "Computed spectrum for g = 0.1 l = 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.2 l = 0\n",
+      "Computed spectrum for g = 0.2 l = 1\n",
       "Computed spectrum for g = 0.2 l = 2\n",
-      "Computed spectrum for g = 0.2 l = 3\n",
-      "Computed spectrum for g = 0.2 l = 4\n",
-      "Computed spectrum for g = 0.2 l = 5\n"
+      "Computed spectrum for g = 0.2 l = 3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.2 l = 4\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Computed spectrum for g = 0.2 l = 5\n",
       "Computed spectrum for g = 0.2 l = 6\n",
       "Computed spectrum for g = 0.2 l = 7\n",
-      "Computed spectrum for g = 0.2 l = 8\n",
+      "Computed spectrum for g = 0.2 l = 8\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Computed spectrum for g = 0.2 l = 9\n"
      ]
     },
@@ -1071,28 +1089,40 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.2 l = 14\n",
+      "Computed spectrum for g = 0.2 l = 14\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Computed spectrum for g = 0.2 l = 15\n",
       "Computed spectrum for g = 0.3 l = 0\n",
-      "Computed spectrum for g = 0.3 l = 1\n"
+      "Computed spectrum for g = 0.3 l = 1\n",
+      "Computed spectrum for g = 0.3 l = 2\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.3 l = 2\n",
-      "Computed spectrum for g = 0.3 l = 3\n",
-      "Computed spectrum for g = 0.3 l = 4\n"
+      "Computed spectrum for g = 0.3 l = 3\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.3 l = 4\n",
       "Computed spectrum for g = 0.3 l = 5\n",
       "Computed spectrum for g = 0.3 l = 6\n",
-      "Computed spectrum for g = 0.3 l = 7\n",
+      "Computed spectrum for g = 0.3 l = 7\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Computed spectrum for g = 0.3 l = 8\n"
      ]
     },
@@ -1102,70 +1132,64 @@
      "text": [
       "Computed spectrum for g = 0.3 l = 9\n",
       "Computed spectrum for g = 0.3 l = 10\n",
-      "Computed spectrum for g = 0.3 l = 11\n"
+      "Computed spectrum for g = 0.3 l = 11\n",
+      "Computed spectrum for g = 0.3 l = 12\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.3 l = 12\n",
-      "Computed spectrum for g = 0.3 l = 13\n",
-      "Computed spectrum for g = 0.3 l = 14\n",
-      "Computed spectrum for g = 0.3 l = 15\n"
+      "Computed spectrum for g = 0.3 l = 13\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.4 l = 0\n",
-      "Computed spectrum for g = 0.4 l = 1\n",
-      "Computed spectrum for g = 0.4 l = 2\n",
-      "Computed spectrum for g = 0.4 l = 3\n"
+      "Computed spectrum for g = 0.3 l = 14\n",
+      "Computed spectrum for g = 0.3 l = 15\n",
+      "Computed spectrum for g = 0.4 l = 0\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.4 l = 1\n",
+      "Computed spectrum for g = 0.4 l = 2\n",
+      "Computed spectrum for g = 0.4 l = 3\n",
       "Computed spectrum for g = 0.4 l = 4\n",
-      "Computed spectrum for g = 0.4 l = 5\n",
-      "Computed spectrum for g = 0.4 l = 6\n"
+      "Computed spectrum for g = 0.4 l = 5\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.4 l = 6\n",
       "Computed spectrum for g = 0.4 l = 7\n",
       "Computed spectrum for g = 0.4 l = 8\n",
-      "Computed spectrum for g = 0.4 l = 9\n"
+      "Computed spectrum for g = 0.4 l = 9\n",
+      "Computed spectrum for g = 0.4 l = 10\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.4 l = 10\n",
       "Computed spectrum for g = 0.4 l = 11\n",
-      "Computed spectrum for g = 0.4 l = 12\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "Computed spectrum for g = 0.4 l = 12\n",
       "Computed spectrum for g = 0.4 l = 13\n",
       "Computed spectrum for g = 0.4 l = 14\n",
-      "Computed spectrum for g = 0.4 l = 15\n",
-      "Computed spectrum for g = 0.5 l = 0\n"
+      "Computed spectrum for g = 0.4 l = 15\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Computed spectrum for g = 0.5 l = 0\n",
       "Computed spectrum for g = 0.5 l = 1\n",
       "Computed spectrum for g = 0.5 l = 2\n",
       "Computed spectrum for g = 0.5 l = 3\n",
@@ -1179,25 +1203,25 @@
       "Computed spectrum for g = 0.5 l = 5\n",
       "Computed spectrum for g = 0.5 l = 6\n",
       "Computed spectrum for g = 0.5 l = 7\n",
-      "Computed spectrum for g = 0.5 l = 8\n"
+      "Computed spectrum for g = 0.5 l = 8\n",
+      "Computed spectrum for g = 0.5 l = 9\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.5 l = 9\n",
       "Computed spectrum for g = 0.5 l = 10\n",
       "Computed spectrum for g = 0.5 l = 11\n",
-      "Computed spectrum for g = 0.5 l = 12\n"
+      "Computed spectrum for g = 0.5 l = 12\n",
+      "Computed spectrum for g = 0.5 l = 13\n",
+      "Computed spectrum for g = 0.5 l = 14\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Computed spectrum for g = 0.5 l = 13\n",
-      "Computed spectrum for g = 0.5 l = 14\n",
       "Computed spectrum for g = 0.5 l = 15\n"
      ]
     }
@@ -1210,7 +1234,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "18dd25b8",
+   "id": "c9477b1b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1220,22 +1244,22 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fb93b221960>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b2888e0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b3f1cc0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b2224d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b2218a0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b222230>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b2209d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b2220e0>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b220970>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b223490>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b223d60>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b220910>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b220e80>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b222d40>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b223340>,\n",
-       " <matplotlib.lines.Line2D at 0x7fb93b220730>]"
+       "[<matplotlib.lines.Line2D at 0x7f8298ce1b70>,\n",
+       " <matplotlib.lines.Line2D at 0x7f829b659b40>,\n",
+       " <matplotlib.lines.Line2D at 0x7f829b659180>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce3d90>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce2b60>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce3dc0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce05e0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce1fc0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce20b0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce2170>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce0e50>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce3070>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce0790>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce13f0>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce1360>,\n",
+       " <matplotlib.lines.Line2D at 0x7f8298ce1de0>]"
       ]
      },
      "execution_count": 15,
@@ -1272,7 +1296,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "17ab5d8c",
+   "id": "8eae8356",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/_sources/toffoli_shor_code.ipynb b/_sources/toffoli_shor_code.ipynb
index 6b85f07..99deea0 100644
--- a/_sources/toffoli_shor_code.ipynb
+++ b/_sources/toffoli_shor_code.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9a4bc897",
+   "id": "ff92ac80",
    "metadata": {},
    "source": [
     "# ToffoliゲートとShorコード\n",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8a5719d",
+   "id": "f7a9e127",
    "metadata": {},
    "source": [
     "## 量子エラー訂正\n",
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "642157dd",
+   "id": "866120c3",
    "metadata": {},
    "source": [
     "## Toffoliゲート\n",
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "9814dfae",
+   "id": "9ba5dde5",
    "metadata": {
     "tags": [
      "remove-input"
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2e36622",
+   "id": "da1c1adc",
    "metadata": {},
    "source": [
     "Toffoliゲートはエラー訂正だけでなく、多重制御ゲートの基本パーツとなるため、さまざまな場面で登場します。また理論的には、ToffoliゲートとHadamardゲートの組み合わせで全ての量子演算が実現できる（普遍ゲートセットとなる）ことから、Toffoliゲートは特別重要な存在です。\n",
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "f81cc717",
+   "id": "b28742a0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "1fc0ac3b",
+   "id": "8a70513a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "748df2b8",
+   "id": "cf30f29d",
    "metadata": {},
    "source": [
     "## CPゲート\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f77d0b95",
+   "id": "b0bef5c9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -223,7 +223,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "4e072f58",
+   "id": "09649d29",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "23f648ad",
+   "id": "1e576a2c",
    "metadata": {},
    "source": [
     "$CP$は`QuantumCircuit`オブジェクトにもメソッド`cp`として備わっているので、以下では（回路図の見やすさを考慮して）標準メソッドを使うことにします。"
@@ -261,7 +261,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64e2d345",
+   "id": "4ebfdf7d",
    "metadata": {},
    "source": [
     "## CCZゲート\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c77354d3",
+   "id": "45b7a18e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "12343467",
+   "id": "7c3cbf25",
    "metadata": {
     "tags": [
      "remove-output"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22c9a67f",
+   "id": "9e5cfae6",
    "metadata": {},
    "source": [
     "## Hadamardゲート\n",
@@ -369,7 +369,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "e2a822d3",
+   "id": "745453be",
    "metadata": {
     "tags": [
      "remove-output"
@@ -407,7 +407,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "cb8df01c",
+   "id": "ac1247c9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "236bb0b7",
+   "id": "6dd3d27d",
    "metadata": {},
    "source": [
     "## Toffoliゲート\n",
@@ -445,7 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "cee9908f",
+   "id": "1465f0da",
    "metadata": {
     "tags": [
      "remove-output"
@@ -485,7 +485,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a0c9357c",
+   "id": "70ff8ef3",
    "metadata": {
     "tags": [
      "remove-output"
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "58d520f5",
+   "id": "271273c2",
    "metadata": {},
    "source": [
     "## 量子エラーとエラー訂正\n",
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "405a42c2",
+   "id": "2a1af56d",
    "metadata": {},
    "source": [
     "### ビット反転（$X$）エラー\n",
@@ -544,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "af78db14",
+   "id": "b67444e0",
    "metadata": {
     "tags": [
      "remove-output"
@@ -553,7 +553,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 789.163x284.278 with 1 Axes>"
       ]
@@ -610,7 +610,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "28028fbc",
+   "id": "2a5f69cd",
    "metadata": {
     "tags": [
      "remove-output"
@@ -619,7 +619,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x50 with 1 Axes>"
       ]
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4073cd6",
+   "id": "56f71e6b",
    "metadata": {},
    "source": [
     "第1と第2量子ビット（Qiskitの順番なので左二桁）の状態が同一である＝第3量子ビットとのエンタングルメントが切れていれば、正しくデコードされています。第0ビットの状態をエラーのない単一量子ビット回路のものと比較します。"
@@ -643,7 +643,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "3915dc9c",
+   "id": "ae84365e",
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72ffd75b",
+   "id": "66e8a672",
    "metadata": {},
    "source": [
     "### 位相反転（$Z$）エラー\n",
@@ -678,7 +678,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "c5b42909",
+   "id": "20c06e2a",
    "metadata": {
     "tags": [
      "remove-output"
@@ -687,7 +687,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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s+/5sRQEemvMCvnPT410eY+tf1+Avua8DAG6ZsBgP3PbLLrchIiJjUlQgcTbw0ZuA5ofXzMbcBNh66HOQUp5q6qqZk83/7HgEt3/jYWSuPoV7pq/Gi1lLO10vpPcAPJP6Nn6Xlof/XfEJTp75P7z/yZtd7v9SUx3W/WkZ/nvpO3hj9ecY2HcYtv71F52u+/hdG7Fp5VFsWnkUzz24DwoUTEvq+nVfx7/4J/Yf3YZNq45jc1oeDp36Cz7Kz+5yO9kMGTIEq1evxpAhPfBxLD9hDkh2ZqoB20BXk+WO5nrX67i6mo/uq/oNA4ZPdO84/iRlM5eTkwNd1zFnzhzRoQhXXXcep+yHMPP6+wAA3xx/NypqzqK0srDDuqMjrsPQgaMAAEGBVsQMm4Bz1cVdHuPjgr0YPew6DB8cDwCYd+Oj2H90W5fbvffJG5g05tsI7dv1RIp/P5qFmdcvQe8gG4ICemF2ygPYf6TrY8gmLCwMS5cuRVhYmOhQhGEOSHZmq4Hhk4CIpO6v//HvXfPLdTUf3WXBoUDyna4zgT1VDw6N/KGi5ixC+w6F5d8TXSqKgsEDhuN8zdUfWa+64MCB49vxjYQ7ujzG+ZoSDBnwn3fkDhkQjaoL5XA6W6+63V9yt2D25GXdGAVQUVOCwV87RldjkFFtbS327duH2torz55udswByc5sNaAoQPwsIHKC9/fdJwyYuAgI6uFvwmQzR26rb7yAn7w+F4tuSceYqEk+OcaJLw6goekiJsff7pP9y8put+PJJ5+E3W4XHYowzAHJzow1oCjAmG8BCd+G197yE5EETLrX9caHno7NnOQG9Y9qd5ZM13Wcry7B4P6dz4rY0HgRP948GzcmzseCaSu7dYzB/YfjXPWZtu/PVRe3OxvYmb0f/w63TrwfFtXSzXEMx/mvHeNKYyAiIvNRFCBiPDBlKRA2yvP99O4HXLcASLgVCOjltfB8is2c5Ab0GYzREdfjr4ddNw8cOLEDYf0jERE2usO6l5rq8F+bZ2PSmNlInflsh+UPvBCPytrSDp+njJmNwtLDKDnven/grv/7X9ySfOV3I9Y3XsCBE9vx7ZQH2n1eUPIx0jZ9q9NtpiUvxF8Pv4VLzfVobm3CvtwtuGWCb96/SEREPZe1LzDhLmDK911z0XX3TF3YKNd2Ny5zTXtiJFJOTULtPXH3JryYtRTbcn6FYGtfpC16vW3Zuj89iClj5+HGxHnY+cH/4LOzH6OxuR4fnNgJALg5eSFSv/UMquvO40LDlwgJ7vjcdrA1BE8u3Iz/zrwTTq0V0eHjkH7PG23LH3lpAn65bA/C+g0DAPz96NuIjZyIyEGx7fZzrroYva4wW2NyzC2YlnwPHl43HgBwS/I9uGFs1/fzERGROdkGui69xt4C1H8JXDgH1FUApccBrdX1NoeYqUDfcCBksHHOwnWGzRwhavAYrP/Rvzpdtmrh5rY/p37rGaR+65lO1zt++h/4zk0rrths3Zjoagg7s2nl0Xbfz7nhYcy54eEO6x07/Q8snv50p/sAgCWzfools356xeUEWK1WJCQkwGq1ig5FGOaAZCdbDagWV7MWMtj1/flTQFMdEGgFRqSIjc1b2MyRV0xLXujzYzx+10afH8PsYmJisHPnTtFhCMUckOxYA+bDe+aIiIiIDIzNHJFE8vLykJSUhLy8PNGhCMMckOxYA+bDZo5IIrquo6WlBbquiw5FGOaAZMcaMB82c0REREQGxgcgJKMGAtMfFx2FOGqg6AiIuidYtaB6xizRYbgluJuTfHd3XzKPn8gdbOYkoyjee9UJEfmOoiiwXeUtKWYn+/iJ3MFKIZJITEwMdu3ahaioKNGhCMMckOxYA+bDZo5IIlarFbGxsV2vaGLMAcmONWA+fACCSCKlpaV49tlnUVra8R26smAOSHasAfNhM0ckkZqaGuzYsQM1NTWiQxGGOSDZsQbMh80cERERkYGxmSMiIiIyMDZzRERERAbGZo5IImFhYXjooYcQFhYmOhRhmAOSHWvAfNjMEUlEURQEBQVBURTRoQjDHJDsWAPmw2aOSCIVFRXYuHEjKioqRIciDHNAsmMNmA+bOSIiIiIDYzNHREREZGBs5oiIiIgMjM0ckUT69euHuXPnol+/fqJDEYY5INmxBsxH0XVdFx0EEXmuoKDA58eIj4/3+TE8Jfv4iVgD7jnwW6CpDujVB/jmD0RH4x08M0ckkaamJpw5cwZNTU2iQxGGOSDZsQbMh80ckUQKCwsxe/ZsFBYWig5FGOaAZMcaMJ8A0QEQEVFHuq6jQXOKDsMtwaqFE9ESCcBmjoioB2rQnBiQ877oMNxSPWMWbBb+WCHyN15mJSIiIjIwNnNEREREBsbz4UQSSUxMRH5+vugwhGIOSHasAfPhmTkiIiIiA2MzRySRoqIiLF68GEVFRaJDEYY5INmxBsyHzRyRRBoaGnDs2DE0NDSIDkUY5oBkxxowHzZzRERERAbGZo6IiIjIwNjMERERERkYmzkiiURERCAjIwMRERGiQxGGOSDZsQbMh/PMEUmkf//+mDdvnugwhGIOSHasAfPhmTkiiVRVVWHr1q2oqqoSHYowzAHJjjVgPmzmiCRSXl6ONWvWoLy8XHQowjAHJDvWgPlI0cxVVlYiPT0do0ePhtVqRVRUFFasWIH6+nosW7YMiqJgw4YNosMkIiIiH2qoBpytrj9rTkDXxMbjLaa/Z+7o0aO47bbb4HA4YLPZMHbsWJSVlWH9+vU4ffp022nmCRMmiA2UiMgHnFsyob39R1hWPgF19q3tlum6Dmfa09Dz8xGwYT2UkdFigiTyIV0Hzp8C7EeB6rP/+bzlEnBwMxCZDEQkA4FWYSFeM1OfmausrMTcuXPhcDiwatUqlJeX4/Dhw3A4HMjIyEB2djZyc3OhKAqSkpJEh0tE5HXqklQgegScm16DXlHZbpm28x3ox09AXXIfGzkyJc0JnNwLnHi3fSN3WeMFoPAAkLsVaKjxe3heY+pm7vHHH4fdbsdjjz2GtWvXIiQkpG1Zeno6kpOT0draiujoaPTt21dgpET+YbPZMHXqVNhsNtGhCCNbDpTAQASkrQIaG+F86ZW2z/WzdmiZb0KJHwN14d3iAiS/k6UGdB0oeB9w5HW9bkM1cGQ70GzQN5yZtpnLz89HVlYWwsLC8Nxzz3W6zsSJEwEAycnJbZ9dbv4mT56MXr16QVEUv8RL5A/R0dHYvHkzoqOjRYcijIw5UGJHQ128CPonh6Fl74XudML5wlpA12FJWwXFYhEdIvmRLDVQWwaUfdr99S/VAMUf+SwcnzJtM7dt2zZomobU1FT06dOn03V69+4NoH0zV1hYiB07diA8PBwpKSl+iZXIX5xOJ+rq6uB0OkWHIoysOVBT7wVGjYLztc3QNv4W+menoC79HpSoSNGhkZ/JUgP2o+5vU3YScLZ4PRSfM20zl5OTAwCYPn36Fdex2+0A2jdzN998M8rLy7Fr1y7MnDnTt0ES+VlBQQFSUlJQUFAgOhRhZM2BEhCAgLSVQHMLtN3ZUMYlQr3rTtFhkQAy1ICzGTh3yv3tWhuBitPej8fXTPs065kzZwAAI0aM6HR5a2srDh48CKB9M6eq3u9vJ02aBIfD4fX9EgHA0qVLu73u5Xml9uzZgyNHjnR7u8zMTDej8h93xg94lgMR49eDgoBNG727U5sNCAwEWluhpEyC4uV/7+Ji46A0N3t1n9Q1s9bAtQjrG4W1D/zLo22fTf8F9h3e5OWIuhYeHo5Dhw55tK1pm7n6+noAwKVLlzpdnpWVhcrKSoSEhGDkyJE+jcXhcKC0tNSnxyB5NTR0/47dxsbGtq/ubNeT//66Mw7AsxwIGb+1FwK9uDtd1+Fc9zLQ2gIMj4L2h7ehTrsZyrChXjtGWXkZ0Njktf1R95i2Bq5Ba4Pn7U1tbY3hxmvaZi48PBzV1dU4fPgwpkyZ0m5ZeXk50tLSAABJSUk+f8ghPDzcp/snuQUHB3d7XavV2vbVne168gu53RkH4FkORIxfDwpChRf3p72zC/qx41C/fz/UKTegdfmP4Fz3MixrM7z2b+CwocN4Zk4As9bAtQgKCERLaxMCA3q5vW2rWi9kvNfSK5i2mZs5cyby8/ORkZGBWbNmIS4uDgCQm5uLJUuWoLLSNd+SPyYL9vS0KVF3uHPfy8mTJ7FlyxbcfvvtSExM7PZ2a9as8SQ0v3D3vh9PciBi/PXOVgzIed8r+9JLS6FtyYQyJg7qogVQLBao96VCe/0NaO/sguU7871ynFOfn4LNYtofKz2WWWvgWn26p3vTknxVoBXY/t5vYfHmaXE/MO0DEOnp6Rg4cCDOnj2LxMREjB8/HrGxsZg8eTJGjRqFGTNmAGh/vxyR2cXFxeHgwYNtv9zISLYc6JoG54svAZoGS9rKtmlI1EULoMTFQtuSCb2M7+iUiSw1EDXB/W2GjYPhGjnAxM1cZGQkDhw4gDlz5sBqtaK4uBihoaHYtGkTsrOzceqU6zEXNnMkk8DAQISGhiIw0ID/WnmJbDnQtu+EnpcP9f77oAwf3va5YrHA8tRKQHPCue5l6LouMEryJ1lqoO9QIMKNlzsFDwBGfMN38fiSaZs5AEhISMDu3btx8eJFXLx4ER999BEefvhh1NfXo7i4GKqqYty4caLDJPKbkpISPProoygpKREdijAy5UAvKYH2xltQEuKh3n1Xh+VK9Aio96VCP/EptHd2CYiQRJClBhQFGDMTGNqNH/PBocB1C4Cg3r6PyxekvLnh5MmT0HUdcXFxnd78uX37dgBAXl5eu++jo6MxadIk/wVK5GUXL17E/v37sXz5ctGhCCNTDpThwxGY/eerrmO59x5Y7r3HTxFRTyBTDagqMPbbwKDRrkmEq4rbL+/dH4icAESMBzx4VqLHkLKZO3HiBIArX2JduHBhp9/ff//9hptrh4iISGaKAgwe7frvUg3w8e+BlkYgsDdw4zLXcqNjM9cJ3jtCRERkPr37A+q/Ox/VYo5GDjD5PXNX0lUzR0RERGQUUp6Zu/zeViLZDBkyBKtXr8aQIUNEhyIMc0CyYw2Yj5TNHJGswsLC3H6Po9kwByQ71oD5SHmZlUhWtbW12LdvH2pra0WHIgxzQLJjDZgPmzkiidjtdjz55JOw2+2iQxGGOSDZsQbMh80cERERkYGxmSMiIiIyMDZzRERERAbGZo5IIlarFQkJCbBaraJDEYY5INmxBsyHU5MQSSQmJgY7d+4UHYZQzAHJjjVgPjwzR0RERGRgbOaIJJKXl4ekpCTk5eWJDkUY5oBkxxowHzZzRBLRdR0tLS3QdV10KMIwByQ71oD58J45IqIeKFi1oHrGLNFhuCVYtYgOgUhKbOaIiHogRVFgs/CfaCLqGi+zEhERERkYf+0jkkhMTAx27dqFqKgo0aEIwxyQ7FgD5sNmjkgiVqsVsbGxosMQijkg2bEGzIeXWYkkUlpaimeffRalpaWiQxGGOSDZsQbMh80ckURqamqwY8cO1NTUiA5FGOaAZMcaMB82c0REREQGxmaOiIiIyMDYzBEREREZGJs5IomoqoqUlBSoqrylzxyQ7FgD5sP/k0QS0TQNubm50DRNdCjCMAckO9aA+bCZIyIiIjIwNnNEREREBsZmjoiIiMjA2MwRSaRfv36YO3cu+vXrJzoUYZgDkh1rwHwUXdd10UEQkecKCgp8foz4+HifH8NTso+fiDXgngO/BZrqgF59gG/+QHQ03sEzc0QSaWpqwpkzZ9DU1CQ6FGGYA5Ida8B82MwRSaSwsBCzZ89GYWGh6FCEYQ5IdqwB8wkQHQAREXWk6zoaNKfoMNwSrFqgKIroMIikw2aOiKgHatCcGJDzvugw3FI9YxZsFv5YIfI3XmYlIiIiMjA2c0REREQGxvPhRBJJTExEfn6+6DCEYg5IdqwB8+GZOSIiIiIDYzNHJJGioiIsXrwYRUVFokMRhjkg2bEGzIfNHJFEGhoacOzYMTQ0NIgORRjmgGTHGjAfNnNEREREBsZmjoiIiMjA2MwRERERGRibOSKJREREICMjAxEREaJDEYY5INmxBsyH88wRSaR///6YN2+e6DCEYg5IdqwB8+GZOSKJVFVVYevWraiqqhIdijDMAcmONWA+bOaIJFJeXo41a9agvLxcdCjCMAckO9aA+bCZIyIiIjIwKZq5yspKpKenY/To0bBarYiKisKKFStQX1+PZcuWQVEUbNiwQXSYRERe59ySiZZbb4e2770Oy3RdR+tTq9EyZx70omL/B0fkJ85WoPwkcGgb0FTv+qy5Hjh9EGiqExubN5j+AYijR4/itttug8PhgM1mw9ixY1FWVob169fj9OnTbfcMTJgwQWygREQ+oC5JhfbhR3Bueg3KxOuhDAprW6btfAf68RNQH1gKZWS0uCCJfOjLYuDTbKDlUvvPdR0o+hdQ/CEwPAUY/U1AUYSEeM1MfWausrISc+fOhcPhwKpVq1BeXo7Dhw/D4XAgIyMD2dnZyM3NhaIoSEpKEh0ukc/ZbDZMnToVNptNdCjCyJYDJTAQAWmrgMZGOF96pe1z/awdWuabUOLHQF14t7gAye9kqoGK08CRHR0bua/SdeDMx0DB+64/G5Gi60YNvWvf/e53sW3bNjz22GP49a9/3WH5hAkTcOzYMYwcORJffPGFgAiJrl1BQYHPjxEfH+/zY3jKrOOvd7ZiQM77Xtuf8423oG3dBsuKH0GZfSucT6yCXlSMgN9sgBIV6ZVjVM+YBZvF9Bd8ehyz1sC1aqoHDr4GaK3d32bsbGDYON/F5CumPTOXn5+PrKwshIWF4bnnnut0nYkTJwIAkpOT2z7bvn077r77bowYMQLBwcGIj4/HM888g7o6E1xUJ+k5nU7U1dXB6XSKDkUYWXOgpt4LjBoF52uboW38LfTPTkFd+j2vNXJkHLLUQOlx9xo5ACg5bMyzc6Zt5rZt2wZN05Camoo+ffp0uk7v3r0BtG/m1q5dC4vFgl/96lfYu3cvfvjDH+I3v/kNZs+eDU3T/BI7ka8UFBQgJSXFL7/J91Sy5kAJCEBA2kqguQXa7mwo4xKh3nWn6LBIABlqQNdczZy76s4DtQacscW058NzcnIAANOnT7/iOna7HUD7Zu7dd9/FoEGD2r6fNm0aBg0ahNTUVHzwwQe4+eab3Y5l0qRJcDgcbm9H1B1Lly7t9rqX55Xas2cPjhw50u3tMjMz3YzKf9wZP+BZDkSMXw8KAjZt9O5ObTYgMBBobYWSMgmK6t3f5+Ni46A0N3t1n9Q1s9bAtehvG4JXHvrEo22feOgn+Oux170cUdfCw8Nx6NAhj7Y1bTN35swZAMCIESM6Xd7a2oqDBw8CaN/MfbWRu2zSpEkAgNLSUo9icTgcHm9L1JWGhoZur9vY2Nj21Z3tevLfX3fGAXiWAyHjt/ZCoBd3p+s6nOteBlpbgOFR0P7wNtRpN0MZNtRrxygrLwMam7y2P+oe09bANdAHWj3etvFSi+HGa9pmrr7eNZHMpUudP8KSlZWFyspKhISEYOTIkVfd1/79+wEACQkJHsUSHh7u0XZE3REcHNztda1Wa9tXd7bryS/kdmccgGc5EDF+PSgIFV7cn/bOLujHjkP9/v1Qp9yA1uU/gnPdy7CszYDipfkYhg0dxjNzApi1Bq5FSG/3cvJVgb3EjPdaegXTPs06duxY5OfnY8OGDVi+fHm7ZeXl5Zg4cSLKy8sxdepUfPDBB1fcT2lpKa677jpMnDgRe/fu9XXYRG5z576XkydPYsGCBdi+fTsSExO7vV1PfpLN3ft+PMmB0Z9m1UtL0fqDx6BEj4DllXVQLBY4t2VBe/0NqD98BJbvzPfKcfg0qxhmrYFr9fFbwIVz7m839UGgd3+vh+NTpn0AYubMmQCAjIwMnDp1qu3z3NxcTJ8+HZWVlQCuPllwXV0d5s+fj6CgIGzZssWn8RL5Q1xcHA4ePIi4uDjRoQgjWw50TYPzxZcATYMlbSUUiwUAoC5aACUuFtqWTOhlBrzjmzwmSw1ETnB/m7BRxmvkABM3c+np6Rg4cCDOnj2LxMREjB8/HrGxsZg8eTJGjRqFGTNmAGh/v9xXXbp0CXPnzkVRURHee+89DB3qvftKiEQJDAxEaGgoAgO9eTeWsciWA237Tuh5+VDvvw/K8OFtnysWCyxPrQQ0J5zrXoZJL9JQJ2SpgSHxbjZmChA92VfR+JZpm7nIyEgcOHAAc+bMgdVqRXFxMUJDQ7Fp0yZkZ2e3na3rrJlraWnBggULcOjQIezduxdjx471d/hEPlFSUoJHH30UJSUlokMRRqYc6CUl0N54C0pCPNS77+qwXIkeAfW+VOgnPoX2zi4BEZIIstSAJRC47m6gV+ezk7WnuCYM7m/QaRdNfXNDQkICdu/e3eHzuro6FBcXQ1VVjBvXfqrny3PT/e1vf8OePXswebJB23SiTly8eBH79+/vcB+pTGTKgTJ8OAKz/3zVdSz33gPLvff4KSLqCWSqgeABQEoq8NnfXK/2QicnoG0DgdhprkusRmXqZu5KTp48CV3XERcX1+FJnuXLl+NPf/oTnn76aQQHB+PDDz9sWxYTE9Pp1CVERETUM1lDgOQ7gcYLQOkJoKHaNalwULDrUmz/CMBLD3QLI2Uzd+LECQCdX2K9/MTq888/j+eff77dstdff93tyRmJiIhIPGtfIGaq6Ch8g83c1xQXF/s5GiIiIiLPmfYBiKu5WjNHZGZDhgzB6tWrMWTIENGhCMMckOxYA+Zj2kmDiWThj5dl9+QJQ806fm9OGuwvnDRYDLPWAHWflGfmiGRVW1uLffv2oba2VnQowjAHJDvWgPmwmSOSiN1ux5NPPgm73S46FGGYA5Ida8B82MwRERERGRibOSIiIiIDYzNHREREZGBs5ogkYrVakZCQAKvVKjoUYZgDkh1rwHz4DDmRRGJiYrBz507RYQjFHJDsWAPmwzNzRERERAbGZo5IInl5eUhKSkJeXp7oUIRhDkh2rAHzYTNHJBFd19HS0gKZX/zCHJDsWAPmw3vmiIh6oGDVguoZs0SH4ZZg1SI6BCIpsZkjIuqBFEXhe06JqFt4mZWIiIjIwPhrH5FEYmJisGvXLkRFRYkORRjmgGTHGjAfNnNEErFarYiNjRUdhlDMAcmONWA+vMxKJJHS0lI8++yzKC0tFR2KMMwByY41YD5s5ogkUlNTgx07dqCmpkZ0KMIwByQ71oD5sJkjIiIiMjA2c0REREQGxmaOiIiIyMDYzBFJJCwsDA899BDCwsJEhyIMc0CyYw2Yj6Lz5WxEREREhsUzc0REREQGxmaOiIiIyMDYzBEREREZGJs5IiIiIgNjM0dERERkYGzmiIiIiAyMzRwRERGRgbGZIyIiIjIwNnNEREREBsZmjoiIiMjA2MwRERERGRibOSIiIiIDYzNHREREZGBs5oiIiIgMjM0cERERkYH9f7TpuiaOPnXSAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 789.163x284.278 with 1 Axes>"
       ]
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "6b47b4df",
+   "id": "3577d41b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -750,7 +750,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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hlETzZJbWAAAAAElFTkSuQmCC",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x50 with 1 Axes>"
       ]
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b6a7366",
+   "id": "e0e6afdc",
    "metadata": {},
    "source": [
     "### Shorコード\n",
@@ -776,7 +776,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "437dd3fb",
+   "id": "e3cf58a0",
    "metadata": {
     "tags": [
      "remove-output"
@@ -785,9 +785,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 956.385x785.944 with 1 Axes>"
+       "<Figure size 872.774x785.944 with 1 Axes>"
       ]
      },
      "execution_count": 17,
@@ -851,7 +851,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "dfa49e4e",
+   "id": "24aaf1ec",
    "metadata": {
     "tags": [
      "remove-output"
@@ -860,7 +860,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x50 with 1 Axes>"
       ]
diff --git a/_sources/vqc_machine_learning.ipynb b/_sources/vqc_machine_learning.ipynb
index 142700d..404030d 100644
--- a/_sources/vqc_machine_learning.ipynb
+++ b/_sources/vqc_machine_learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "080364b3",
+   "id": "5c4e7c6d",
    "metadata": {},
    "source": [
     "# 量子機械学習を使った新しい素粒子現象の探索"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7c54dda",
+   "id": "a076e6f3",
    "metadata": {},
    "source": [
     "この実習では、**量子・古典ハイブリッドアルゴリズム**の応用である**量子機械学習**の基本的な実装を学んだのち、その活用例として、**素粒子実験での新粒子探索**への応用を考えます。ここで学ぶ量子機械学習の手法は、変分量子アルゴリズムの応用として提案された、**量子回路学習**と呼ばれる学習手法{cite}`quantum_circuit_learning`です。\n",
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b25e505b",
+   "id": "a0203279",
    "metadata": {},
    "source": [
     "## はじめに <a id='introduction'></a>\n",
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a2de3be",
+   "id": "7f6b7d80",
    "metadata": {},
    "source": [
     "## 機械学習と深層学習 <a id='ml'></a>\n",
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e4a2d87d",
+   "id": "6c964a6e",
    "metadata": {},
    "source": [
     "## 量子回路学習<a id='qml'></a>\n",
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "73af64fc",
+   "id": "0285be30",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "42560146",
+   "id": "5d84899b",
    "metadata": {},
    "source": [
     "## 初歩的な例<a id='example'></a>\n",
@@ -174,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "347b6a99",
+   "id": "a28ee2b2",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d6339bf",
+   "id": "57f8a25b",
    "metadata": {},
    "source": [
     "### 量子状態の生成<a id='func_state_preparation'></a>\n",
@@ -231,7 +231,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "59111062",
+   "id": "b1a3b172",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "76b947f8",
+   "id": "8d77f86a",
    "metadata": {},
    "source": [
     "### 変分フォームを使った状態変換<a id='func_variational_form'></a>\n",
@@ -302,7 +302,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f60fffcc",
+   "id": "288744ed",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1eb16a70",
+   "id": "4c6a6b91",
    "metadata": {},
    "source": [
     "### 測定とモデル出力<a id='func_measurement'></a>\n",
@@ -384,7 +384,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "7bd05a05",
+   "id": "5cabe9bd",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -421,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "4f8fa36e",
+   "id": "d7925760",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bfc45f15",
+   "id": "3c013db3",
    "metadata": {},
    "source": [
     "コスト関数$L$として、モデルの予測値$y(x_i, \\theta)$と真の値$y_i$の平均2乗誤差の総和を使っています。\n",
@@ -471,7 +471,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "5ea24242",
+   "id": "a174e8f9",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -495,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "4e3e6ec4",
+   "id": "abfec63a",
    "metadata": {
     "tags": [
      "remove-input"
@@ -512,7 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "865e5877",
+   "id": "9b5b6a7c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -548,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "89a3ee94",
+   "id": "80498c04",
    "metadata": {
     "tags": [
      "remove-input"
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acae5e6a",
+   "id": "0f80c147",
    "metadata": {},
    "source": [
     "コスト値の推移をプロットします。"
@@ -576,13 +576,13 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "0e812c03",
+   "id": "e4e83ea4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff4b82358d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f7a3429f310>]"
       ]
      },
      "execution_count": 11,
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a6c23f60",
+   "id": "c27dc067",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -622,7 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "84999c2b",
+   "id": "800fb95c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -634,7 +634,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -657,7 +657,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b60269a",
+   "id": "89a6e168",
    "metadata": {},
    "source": [
     "生成された図を確認してください。ノイズを印加した学習データの分布から、元の関数$f(x)=x^3$をおおよそ導き出せていることが分かると思います。\n",
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c16360c",
+   "id": "f0d5f98b",
    "metadata": {},
    "source": [
     "## 素粒子現象の探索への応用<a id='susy'></a>\n",
@@ -688,7 +688,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21a22125",
+   "id": "32d0dac8",
    "metadata": {},
    "source": [
     "### 学習データの準備<a id='susy_data'></a>\n",
@@ -701,7 +701,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "68acdd0e",
+   "id": "9131dac6",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "580424b5",
+   "id": "6ae879ef",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -806,7 +806,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "990ddbd7",
+   "id": "b7921fc3",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -836,7 +836,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ecd04c13",
+   "id": "0aac7495",
    "metadata": {},
    "source": [
     "### 変分フォームを使った状態変換<a id='susy_variational_form'></a>\n",
@@ -853,7 +853,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "ffcfe5cd",
+   "id": "dbf26e49",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -883,7 +883,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4070c41",
+   "id": "fa9f4e50",
    "metadata": {},
    "source": [
     "### 測定とモデル出力<a id='susy_measurement'></a>\n",
@@ -896,7 +896,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "0426507c",
+   "id": "24f72d47",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -956,7 +956,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "39d4a32a",
+   "id": "08562d7e",
    "metadata": {
     "tags": [
      "remove-input"
@@ -972,7 +972,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "39883a86",
+   "id": "40c93928",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1026,7 +1026,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "fecff7d0",
+   "id": "82e3c704",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "3269ac0b",
+   "id": "e0620485",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1098,7 +1098,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7e181e86",
+   "id": "719ba451",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/vqe.ipynb b/_sources/vqe.ipynb
index 5b59525..688bc88 100644
--- a/_sources/vqe.ipynb
+++ b/_sources/vqe.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9223c361",
+   "id": "2b22682f",
    "metadata": {},
    "source": [
     "# 変分法と変分量子固有値ソルバー法を学習する"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f3abcb7a",
+   "id": "0d78b90b",
    "metadata": {},
    "source": [
     "この実習では、変分法の基本的な考え方と、その方法に基づいた変分量子アルゴリズムと呼ばれる量子計算の手法を学びます。特に、量子計算と古典計算を組み合わせた「**量子・古典ハイブリッドアルゴリズム**」としての変分量子アルゴリズムに着目します。この手法を用いて、近似的な固有値計算を可能にする**変分量子固有値ソルバー法**と呼ばれる方法へ拡張していきます。\n",
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a137262",
+   "id": "311c715c",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96d2f16a",
+   "id": "da1d8272",
    "metadata": {},
    "source": [
     "## 量子力学における変分法\n",
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "363dadc2",
+   "id": "7048e2c1",
    "metadata": {},
    "source": [
     "(vqa)=\n",
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f0217a9",
+   "id": "20f13149",
    "metadata": {},
    "source": [
     "### 変分量子回路\n",
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e3939c35",
+   "id": "f0818c7f",
    "metadata": {},
    "source": [
     "### 単純な変分フォーム\n",
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "63604daf",
+   "id": "e5259d58",
    "metadata": {},
    "source": [
     "### パラメータの最適化\n",
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "22a67975",
+   "id": "948ba501",
    "metadata": {},
    "source": [
     "### 変分フォームを使った実例\n",
@@ -171,7 +171,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "4ffa14f3",
+   "id": "a4548487",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20f687eb",
+   "id": "bf0a10de",
    "metadata": {},
    "source": [
     "最初に、ターゲットとなる量子状態ベクトルをランダムに生成する関数と、状態ベクトルから$X, Y, Z$の期待値を計算する関数を定義します。状態ベクトルはQiskitのStatevectorというクラスで表現し、パウリ演算子にはSparsePauliOpを使用します。"
@@ -203,7 +203,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0373bf36",
+   "id": "99b40fd1",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -217,9 +217,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<X> = 0.75\n",
-      "<Y> = 0.43301270189221924\n",
-      "<Z> = 0.5000000000000002\n"
+      "<X> = 0.7499999999999998\n",
+      "<Y> = 0.43301270189221913\n",
+      "<Z> = 0.5\n"
      ]
     }
    ],
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fc6fcf58",
+   "id": "4d85aef6",
    "metadata": {},
    "source": [
     "次に、変分フォーム回路を定義します。このとき、$U$ゲートの回転角として、具体的な数値を設定せず、QiskitのParameterというオブジェクトを利用します。Parameterはあとから数値を代入できる名前のついた箱として使えます。"
@@ -253,7 +253,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "407f6645",
+   "id": "99560ffc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -263,7 +263,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f514d153610>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f8cc7177ca0>"
       ]
      },
      "execution_count": 3,
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8927114",
+   "id": "06621eb8",
    "metadata": {},
    "source": [
     "Parameterに値を代入するには、回路の`bind_parameters`メソッドを利用します。"
@@ -290,7 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "0d27b767",
+   "id": "f8cf67c5",
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "2743265c",
+   "id": "5820bfd3",
    "metadata": {},
    "outputs": [
     {
@@ -335,7 +335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4f446d0",
+   "id": "3be4b633",
    "metadata": {},
    "source": [
     "変分フォーム回路が作る状態における$X, Y, Z$の期待値を測定するための回路を定義します。"
@@ -344,7 +344,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3bd73c3c",
+   "id": "b2b297ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "87567ffa",
+   "id": "32569b5f",
    "metadata": {},
    "source": [
     "それぞれの回路を通常通りバックエンドの`run()`メソッドで実行し、結果から期待値を計算する関数を定義します。"
@@ -377,16 +377,16 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "b066e181",
+   "id": "cd1d56b5",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<X> = 0.7592\n",
-      "<Y> = 0.4408\n",
-      "<Z> = 0.4996\n"
+      "<X> = 0.7582\n",
+      "<Y> = 0.4432\n",
+      "<Z> = 0.4852\n"
      ]
     }
    ],
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "570bd135",
+   "id": "3fd6a81b",
    "metadata": {},
    "source": [
     "最小化する目的関数を定義します。"
@@ -421,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "18166ab6",
+   "id": "04aa9e37",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "67136195",
+   "id": "091c650b",
    "metadata": {},
    "source": [
     "最適化には使用しませんが、解を得たあとで変分フォームの終状態とターゲット状態とのフィデリティ$|\\langle \\psi_0 | \\psi(\\theta, \\phi) \\rangle|^2$を計算する関数も定義しておきます。厳密に最適化が成功すれば、この関数の返り値は1になります。"
@@ -453,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "ab0ead29",
+   "id": "c3c5fb75",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "445e0741",
+   "id": "baa1c2e8",
    "metadata": {},
    "source": [
     "最後にCOBYLAオプティマイザーのインスタンスを作成し、アルゴリズムを実行します。"
@@ -481,7 +481,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "14bb3050",
+   "id": "23a78864",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "ed6aa019",
+   "id": "b3518de9",
    "metadata": {
     "tags": [
      "remove-input"
@@ -517,7 +517,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "60f285dc",
+   "id": "642379ef",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -552,7 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "fec1a1e8",
+   "id": "b93652de",
    "metadata": {
     "tags": [
      "remove-input"
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "269e7fa3",
+   "id": "e7086c07",
    "metadata": {},
    "source": [
     "最適化プロセスにおけるロス（目的関数の返り値）の推移をプロットします。"
@@ -579,7 +579,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "60743e5d",
+   "id": "a64b080a",
    "metadata": {},
    "outputs": [
     {
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "62e56d4a",
+   "id": "c9c6b2c7",
    "metadata": {},
    "source": [
     "`optimizer.minimize()`の返り値の`min_result`から最適化過程の様々な情報（目的関数の呼び出し回数や最適化に要したステップ数など）にアクセスできます。特に、最適化されたパラメータ値が`min_result.x`から得られるので、それを使ってフィデリティを計算してみます。"
@@ -608,13 +608,13 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "a730e7b8",
+   "id": "76c20117",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9998472042379072"
+       "0.9998472042379076"
       ]
      },
      "execution_count": 15,
@@ -628,7 +628,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "e4fdeb93",
+   "id": "f02c0734",
    "metadata": {},
    "source": [
     "ショット数が有限なので統計誤差の影響から最適パラメータは厳密解とは一致しません。ショット数やステップ数を変えて、解の一致具合を確認してみてください。"
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c82076c5",
+   "id": "6b7f02d7",
    "metadata": {},
    "source": [
     "#### Estimatorの利用\n",
@@ -647,13 +647,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "f5fd90ae",
+   "id": "56237dd2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.7398, 0.432 , 0.497 ])"
+       "array([0.7458, 0.42  , 0.5012])"
       ]
      },
      "execution_count": 16,
@@ -679,7 +679,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d178feaf",
+   "id": "fabc5838",
    "metadata": {},
    "source": [
     "Estimatorを使った目的関数を定義します。"
@@ -688,7 +688,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "87ac6972",
+   "id": "6663eb5a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,7 +709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41a7909b",
+   "id": "1c868277",
    "metadata": {},
    "source": [
     "上の目的関数を最適化します。"
@@ -718,7 +718,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "d4fe3dd1",
+   "id": "ab94c92f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +736,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "28cfe887",
+   "id": "30456ecd",
    "metadata": {
     "tags": [
      "remove-input"
@@ -753,7 +753,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "1967dd56",
+   "id": "1e503178",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -788,7 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "fa1e388a",
+   "id": "b7081d2a",
    "metadata": {
     "tags": [
      "remove-input"
@@ -806,13 +806,13 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "e9295995",
+   "id": "eb04b235",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9990917238925524"
+       "0.9990917238925522"
       ]
      },
      "execution_count": 22,
@@ -826,7 +826,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c376d20d",
+   "id": "fbab2b4b",
    "metadata": {},
    "source": [
     "### エンタングルメントの導入\n",
@@ -845,7 +845,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "de6a4861",
+   "id": "147dc8db",
    "metadata": {
     "tags": [
      "remove-output"
@@ -855,7 +855,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f514934f370>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f8cc322fb20>"
       ]
      },
      "execution_count": 23,
@@ -875,7 +875,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "2a776d73",
+   "id": "ec13b32a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -900,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "dc163be7",
+   "id": "01208ef7",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -931,7 +931,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "4ac8200b",
+   "id": "4c0f15e4",
    "metadata": {
     "tags": [
      "remove-input"
@@ -948,7 +948,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "d8581bfd",
+   "id": "a19b73d7",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -983,7 +983,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "809a9009",
+   "id": "807060a6",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1001,7 +1001,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "e022e98a",
+   "id": "0f8b1c73",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1028,7 +1028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c972c6e",
+   "id": "7ce67858",
    "metadata": {},
    "source": [
     "やってみると分かりますが、結果は1量子ビットの場合と比べて良くないですね。どうすれば良くなるでしょうか？（やり方は複数あると思います）"
@@ -1036,7 +1036,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5155646f",
+   "id": "761e6f91",
    "metadata": {},
    "source": [
     "**一つの解決策：変分フォームにエンタングルメントを導入する**\n",
@@ -1053,7 +1053,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ee069c9",
+   "id": "4909be51",
    "metadata": {},
    "source": [
     "2量子ビットの一般の状態では2つのビットがエンタングルしているので、変分フォームに2量子ビットゲートを入れると近似精度が良くなるのはある意味当然です。例えば、ベル状態（[CHSH不等式の破れを確認する](https://utokyo-icepp.github.io/qc-workbook/chsh_inequality.html#id14)を参照）を再現したいときにこの状況をクリアに見ることができます。上で\n",
@@ -1081,7 +1081,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2ef0d75c",
+   "id": "7a77c622",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1147,7 +1147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38e99418",
+   "id": "91050af2",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1168,7 +1168,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "00008aea",
+   "id": "14374c0d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1180,7 +1180,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "377050fa",
+   "id": "3fb6c8a0",
    "metadata": {},
    "outputs": [
     {
@@ -1227,7 +1227,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "b7eafaa9",
+   "id": "fe2e744c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1259,7 +1259,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "6e7c2ce8",
+   "id": "8f22c5ce",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1276,7 +1276,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "94a3082f",
+   "id": "8a67f890",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -1306,7 +1306,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "b907889a",
+   "id": "9772d5b9",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "24dec6dc",
+   "id": "ad830c1d",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1355,7 +1355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3b7df8dc",
+   "id": "1a5d9c48",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/vqe_tracking.ipynb b/_sources/vqe_tracking.ipynb
index 3aca65c..a2f94ea 100644
--- a/_sources/vqe_tracking.ipynb
+++ b/_sources/vqe_tracking.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2da4d5fb",
+   "id": "9de91b09",
    "metadata": {},
    "source": [
     "# 【課題】高エネルギー実験で生成された荷電粒子の飛跡を見つける"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "091b8abd",
+   "id": "c80b9cd6",
    "metadata": {},
    "source": [
     "この課題では、変分量子固有値ソルバー法を物理実験に応用することを考えてみます。特に高エネルギー物理の実験に着目し、その必須技術である「**荷電粒子飛跡の再構成**」を変分量子固有値ソルバー法を使って実現することを目指します。\n",
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ac7b8ee",
+   "id": "79ca53aa",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c25aa448",
+   "id": "ba312cc4",
    "metadata": {},
    "source": [
     "(hep)=\n",
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04691895",
+   "id": "1179127f",
    "metadata": {},
    "source": [
     "(hep_detect)=\n",
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9534c723",
+   "id": "f2e122de",
    "metadata": {},
    "source": [
     "(ML_challenge)=\n",
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80eccabf",
+   "id": "ae92efb7",
    "metadata": {},
    "source": [
     "(tracking)=\n",
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f0f5bca8",
+   "id": "77b3e92a",
    "metadata": {},
    "source": [
     "まず、必要なライブラリを最初にインポートします。"
@@ -110,7 +110,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "cf4e199a",
+   "id": "1edc7709",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4c39b49d",
+   "id": "1f495b09",
    "metadata": {},
    "source": [
     "(hamiltonian_form)=\n",
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4183aea4",
+   "id": "bf34bc03",
    "metadata": {},
    "source": [
     "(pre_processing)=\n",
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57733b5d",
+   "id": "2a69aa66",
    "metadata": {},
    "source": [
     "#### QUBO\n",
@@ -200,13 +200,13 @@
     "\n",
     "上のような形式の最適化問題を**QUBO**（*Quadratic Unconstrained Binary Optimization*、2次制約無し2値最適化）と呼びます。一見特殊な形式ですが、実は様々な最適化問題（例えば有名な巡回セールスマン問題なども）がQUBOの形に落とし込めることが知られています。また、ここでは直接関係しませんが、量子アニーリングマシンと呼ばれるタイプの量子コンピュータでは、QUBOを解くことが動作の基本です。\n",
     "\n",
-    "それでは、まずスコア$a_{i}$と$b_{ij}$を読み出しましょう。この後のプログラミングを簡単にするため、二つのスコアセットは一つの二次元配列`b`に記録されており、`b[i, i]`が$a_i$に対応するようになっています。"
+    "それでは、まずスコア$a_{i}$と$b_{ij}$を読み出しましょう。"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "2cc2153a",
+   "id": "ba78c821",
    "metadata": {},
    "outputs": [
     {
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5a259e4",
+   "id": "34b7a6f2",
    "metadata": {},
    "source": [
     "#### Ising形式\n",
@@ -254,7 +254,7 @@
     "H(h, J, s) = \\sum_{i=1}^N h_i Z_i + \\sum_{i=1}^N \\sum_{j<i}^N J_{ij} Z_i Z_j + \\text{(constant)}\n",
     "$$\n",
     "\n",
-    "が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の$constant$はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。\n",
+    "が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の$\\text{constant}$はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。\n",
     "\n",
     "以下のセルで、上の処方に従ってIsingハミルトニアンの係数$h_i$と$J_{ij}$を計算してください。"
    ]
@@ -262,7 +262,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "c46f6219",
+   "id": "8f59b50b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fa2a768",
+   "id": "ceaa84b6",
    "metadata": {},
    "source": [
     "次に、この係数をもとに、VQEに渡すハミルトニアンをSparsePauliOpとして定義します。{ref}`vqe_imp`ではSparsePauliOpは単一のパウリ積$ZXY$を表現するのに使いましたが、実はパウリ積の和も同じクラスを使って表現できます。例えば\n",
@@ -305,7 +305,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "9143887c",
+   "id": "9752d1af",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fc8e54f",
+   "id": "430baa72",
    "metadata": {},
    "source": [
     "(tracking_vqe)=\n",
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "8b23d9bc",
+   "id": "fc429841",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1f1ba1e",
+   "id": "3796d5e9",
    "metadata": {},
    "source": [
     "`optimal_segments_diag`のリストで1になっている量子ビットが、目的関数を最小化するセグメントの選択に対応します。\n",
@@ -407,7 +407,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "6aa7376b",
+   "id": "269c8002",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0191c7a6",
+   "id": "f05fbcdd",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -486,7 +486,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "d5c195c4",
+   "id": "12b092b3",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -549,7 +549,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d109ad7",
+   "id": "44b68d82",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/_sources/vqe_tracking.md b/_sources/vqe_tracking.md
index da290d6..ece68aa 100644
--- a/_sources/vqe_tracking.md
+++ b/_sources/vqe_tracking.md
@@ -161,7 +161,7 @@ $$
 
 上のような形式の最適化問題を**QUBO**（*Quadratic Unconstrained Binary Optimization*、2次制約無し2値最適化）と呼びます。一見特殊な形式ですが、実は様々な最適化問題（例えば有名な巡回セールスマン問題なども）がQUBOの形に落とし込めることが知られています。また、ここでは直接関係しませんが、量子アニーリングマシンと呼ばれるタイプの量子コンピュータでは、QUBOを解くことが動作の基本です。
 
-それでは、まずスコア$a_{i}$と$b_{ij}$を読み出しましょう。この後のプログラミングを簡単にするため、二つのスコアセットは一つの二次元配列`b`に記録されており、`b[i, i]`が$a_i$に対応するようになっています。
+それでは、まずスコア$a_{i}$と$b_{ij}$を読み出しましょう。
 
 ```{code-cell} ipython3
 # スコアの読み込み
@@ -189,7 +189,7 @@ $$
 H(h, J, s) = \sum_{i=1}^N h_i Z_i + \sum_{i=1}^N \sum_{j<i}^N J_{ij} Z_i Z_j + \text{(constant)}
 $$
 
-が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の$constant$はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。
+が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の$\text{constant}$はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。
 
 以下のセルで、上の処方に従ってIsingハミルトニアンの係数$h_i$と$J_{ij}$を計算してください。
 
diff --git a/circuit_from_scratch.html b/circuit_from_scratch.html
index 26caef8..6d9f666 100644
--- a/circuit_from_scratch.html
+++ b/circuit_from_scratch.html
@@ -1077,7 +1077,7 @@ <h4><a class="toc-backref" href="#id34">測定結果に基づいた分岐</a><a
 </div>
 <div class="cell_output docutils container">
 <div class="output text_latex math notranslate nohighlight">
-\[\displaystyle | \text{final} \rangle =  - 0.383 |0:10\rangle + 0.924 |1:10\rangle\]</div>
+\[\displaystyle | \text{final} \rangle =  - 0.556 |0:11\rangle + 0.831 |1:11\rangle\]</div>
 </div>
 </div>
 </section>
@@ -1281,7 +1281,7 @@ <h4><a class="toc-backref" href="#id38">SWAPテスト</a><a class="headerlink" h
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;qiskit.circuit.instructionset.InstructionSet at 0x7f980ec17f70&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;qiskit.circuit.instructionset.InstructionSet at 0x7f315ec3c700&gt;
 </pre></div>
 </div>
 <img alt="_images/f71b820845c845681486032617e7ecf6877842e09241d21fc5db1a4839956522.png" src="_images/f71b820845c845681486032617e7ecf6877842e09241d21fc5db1a4839956522.png" />
@@ -1384,7 +1384,7 @@ <h4><a class="toc-backref" href="#id38">SWAPテスト</a><a class="headerlink" h
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.20437
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.203444
 </pre></div>
 </div>
 </div>
@@ -1482,7 +1482,7 @@ <h4><a class="toc-backref" href="#id38">SWAPテスト</a><a class="headerlink" h
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.202728
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.203051
 </pre></div>
 </div>
 </div>
@@ -1584,7 +1584,7 @@ <h2><a class="toc-backref" href="#id40">複製不可能定理とテレポーテ
 </div>
 <div class="cell_output docutils container">
 <div class="output text_latex math notranslate nohighlight">
-\[\displaystyle | \text{final} \rangle = 0.939 |0:1:0\rangle + 0.343e^{0.57 \cdot \pi i} |1:1:0\rangle\]</div>
+\[\displaystyle | \text{final} \rangle = 0.939 |0:0:0\rangle + 0.343e^{0.57 \cdot \pi i} |1:0:0\rangle\]</div>
 </div>
 </div>
 <p>上のセルを何度か実行すると、右二つのレジスタは実行するごとにランダムに違う値を取るものの、二つの項で共通で、一番左のレジスタが<span class="math notranslate nohighlight">\(\ket{\text{in}}\)</span>と同じ状態にあることがわかります。このようにテレポーテーションが実際に起こることを、上の回路を元に数式でも確認してみるといいでしょう。</p>
diff --git a/dynamics_simulation.html b/dynamics_simulation.html
index b9050ec..579e508 100644
--- a/dynamics_simulation.html
+++ b/dynamics_simulation.html
@@ -882,7 +882,7 @@ <h3><a class="toc-backref" href="#id29">回路実装</a><a class="headerlink" hr
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/ffea34c8c352ca3d5db4e5560e99a8d31f9c10fd51ebd34e1a887f80fbca7f2a.png" src="_images/ffea34c8c352ca3d5db4e5560e99a8d31f9c10fd51ebd34e1a887f80fbca7f2a.png" />
+<img alt="_images/d0c7122e2d79b292355f21d14413f1b79d4d5ce20584c6bc326f7248fe422d2c.png" src="_images/d0c7122e2d79b292355f21d14413f1b79d4d5ce20584c6bc326f7248fe422d2c.png" />
 </div>
 </div>
 <p>ビット0でのスピンの不整合が徐々に他のビットに伝搬していく様子が観察できました。</p>
diff --git a/grover.html b/grover.html
index 0aa1cf7..c9cce57 100644
--- a/grover.html
+++ b/grover.html
@@ -964,7 +964,7 @@ <h2><a class="toc-backref" href="#id18">はじめに</a><a class="headerlink" hr
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/5faea17ab86242754a66ece5a58edda3e80b91a41e72c3c8df39e6ef6f327238.png" src="_images/5faea17ab86242754a66ece5a58edda3e80b91a41e72c3c8df39e6ef6f327238.png" />
+<img alt="_images/20342afba7619d375b731fbb61537a03ac31e98d2f52b6e7da46a6d60f5b7a33.png" src="_images/20342afba7619d375b731fbb61537a03ac31e98d2f52b6e7da46a6d60f5b7a33.png" />
 </div>
 </div>
 <p>正しい答え<span class="math notranslate nohighlight">\(\ket{45}\)</span>をより高い確率で測定できていることが分かりますね。</p>
@@ -999,7 +999,7 @@ <h2><a class="toc-backref" href="#id18">はじめに</a><a class="headerlink" hr
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/86e199fac37b02f4823f5f5612cbd04c044d64ee2f725b11fd973c63d7ca44a1.png" src="_images/86e199fac37b02f4823f5f5612cbd04c044d64ee2f725b11fd973c63d7ca44a1.png" />
+<img alt="_images/055793c015df4e8c4d9c27f101c6359f7c1c8af82bf43868259e38268bb52c4a.png" src="_images/055793c015df4e8c4d9c27f101c6359f7c1c8af82bf43868259e38268bb52c4a.png" />
 </div>
 </div>
 <p>この図から、グローバー反復を5~6回程度繰り返すことで、正しい答えを最も高い確率で測定できることが分かりますね。計算で求めた検索に必要な反復回数と一致しているかどうか、確認してみてください。</p>
@@ -1084,7 +1084,7 @@ <h2><a class="toc-backref" href="#id18">はじめに</a><a class="headerlink" hr
      └───┘└───┘└───┘└───┘└───┘└───┘└───┘└───┘
 </pre></div>
 </div>
-<img alt="_images/f354e197d8bcba7a7baab4d6fe38ec89afc4bb475d76216a2f7671106de0badd.png" src="_images/f354e197d8bcba7a7baab4d6fe38ec89afc4bb475d76216a2f7671106de0badd.png" />
+<img alt="_images/2cd0acc9f56c376d08865caaa6ed1d3ec55eaff59d6de8d67673e8f78fade098.png" src="_images/2cd0acc9f56c376d08865caaa6ed1d3ec55eaff59d6de8d67673e8f78fade098.png" />
 </div>
 </div>
 <p>複数解の場合、確率が最大になる反復回数が単一解の場合より減っていますね。予想と合っているでしょうか？</p>
diff --git a/numerics.html b/numerics.html
index f5fce42..26bbcdb 100644
--- a/numerics.html
+++ b/numerics.html
@@ -540,7 +540,7 @@ <h1>予備知識：数値表現<a class="headerlink" href="#id1" title="Permalin
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f41a0345690&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f924c331540&gt;
 </pre></div>
 </div>
 <img alt="_images/8fe7f6cec002f84c92a9a309fc997cb87d2f0c92efbc0bed59c6a03862da1888.png" src="_images/8fe7f6cec002f84c92a9a309fc997cb87d2f0c92efbc0bed59c6a03862da1888.png" />
diff --git a/python_intro.html b/python_intro.html
index ae50918..382a2e7 100644
--- a/python_intro.html
+++ b/python_intro.html
@@ -1681,7 +1681,7 @@ <h3><a class="toc-backref" href="#id42">Jupyter</a><a class="headerlink" href="#
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Using matplotlib backend: &lt;object object at 0x7f581d0b2ac0&gt;
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Using matplotlib backend: &lt;object object at 0x7f983f096ac0&gt;
 </pre></div>
 </div>
 </div>
diff --git a/reports/extreme_simd.err.log b/reports/extreme_simd.err.log
deleted file mode 100644
index 51d51ac..0000000
--- a/reports/extreme_simd.err.log
+++ /dev/null
@@ -1,51 +0,0 @@
-Traceback (most recent call last):
-  File "/usr/local/lib/python3.10/dist-packages/jupyter_cache/executors/utils.py", line 58, in single_nb_execution
-    executenb(
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/client.py", line 1204, in execute
-    return NotebookClient(nb=nb, resources=resources, km=km, **kwargs).execute()
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/util.py", line 84, in wrapped
-    return just_run(coro(*args, **kwargs))
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/util.py", line 62, in just_run
-    return loop.run_until_complete(coro)
-  File "/usr/lib/python3.10/asyncio/base_events.py", line 646, in run_until_complete
-    return future.result()
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/client.py", line 663, in async_execute
-    await self.async_execute_cell(
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/client.py", line 965, in async_execute_cell
-    await self._check_raise_for_error(cell, cell_index, exec_reply)
-  File "/usr/local/lib/python3.10/dist-packages/nbclient/client.py", line 862, in _check_raise_for_error
-    raise CellExecutionError.from_cell_and_msg(cell, exec_reply_content)
-nbclient.exceptions.CellExecutionError: An error occurred while executing the following cell:
-------------------
-# オリジナルの回路をトランスパイルする。optimization_level=3は自動設定のうち、最も効率のいい回路を作る
-# フェイクバックエンドの場合、少し時間がかかるので気長に待ってください
-print('Transpiling the original circuit with standard settings')
-circuit_original_tr = transpile(circuit_original, backend=backend, optimization_level=3)
-
-# 効率化した回路をトランスパイルする。マシンのトポロジーに従い、最も合計エラー率の低い量子ビット列にマッピングする
-print('Transpiling the optimized circuit with trivial mapping onto a chain of qubits')
-initial_layout = find_best_chain(backend, n1 + n2 + n3)
-circuit_optimized_tr = transpile(circuit_optimized, backend=backend,
-                                 routing_method='basic', initial_layout=initial_layout,
-                                 optimization_level=3)
-
-# count_opsは回路に含まれる基本ゲートの数を辞書として返す
-nops_orig = circuit_original_tr.count_ops()
-nops_opt = circuit_optimized_tr.count_ops()
-
-print(f'Number of operations in the original circuit: {circuit_original_tr.size()}')
-print(f'  Breakdown: N(Rz)={nops_orig["rz"]}, N(X)={nops_orig["x"]}, N(SX)={nops_orig["sx"]}, N(CNOT)={nops_orig["cx"]}')
-print(f'Number of operations in the optimized circuit: {circuit_optimized_tr.size()}')
-print(f'  Breakdown: N(Rz)={nops_opt["rz"]}, N(X)={nops_opt["x"]}, N(SX)={nops_opt["sx"]}, N(CNOT)={nops_opt["cx"]}')
-------------------
-
----------------------------------------------------------------------------
-KeyError                                  Traceback (most recent call last)
-Cell In[11], line 20
-     18 print(f'  Breakdown: N(Rz)={nops_orig["rz"]}, N(X)={nops_orig["x"]}, N(SX)={nops_orig["sx"]}, N(CNOT)={nops_orig["cx"]}')
-     19 print(f'Number of operations in the optimized circuit: {circuit_optimized_tr.size()}')
----> 20 print(f'  Breakdown: N(Rz)={nops_opt["rz"]}, N(X)={nops_opt["x"]}, N(SX)={nops_opt["sx"]}, N(CNOT)={nops_opt["cx"]}')
-
-KeyError: 'x'
-KeyError: 'x'
-
diff --git a/searchindex.js b/searchindex.js
index 580289e..327d986 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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"\u305d\u306e\u5f8c": 6, "\u3068\u66f8\u304b\u308c\u3066\u3044\u308b\u6f14\u7b97\u3092\u7e70\u308a\u8fd4\u3057\u9069\u7528\u3057\u307e\u3059": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u53cd\u5fa9": 6, "\u3068\u3082\u547c\u3070\u308c\u308b\u30e6\u30cb\u30bf\u30ea\u30fc\u6f14\u7b97\u3067": 6, "\u4ee5\u4e0b\u306e\u3088\u3046\u306a4\u3064\u306e\u30b9\u30c6\u30c3\u30d7\u304b\u3089\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059": 6, "\u305d\u308c\u305e\u308c\u4e0a\u3067\u8aac\u660e\u3057\u305f\u89e3": 6, "\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30aa\u30e9\u30af\u30eb\u30680\u4ee5\u5916\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30aa\u30e9\u30af\u30eb\u3067\u3059": 6, "\u56de\u8def\u306e\u6700\u521d\u306b\u3042\u308bhadamard\u6f14\u7b97\u3068\u5408\u308f\u305b\u3066": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u53cd\u5fa9\u30921\u56de\u5b9f\u884c\u3059\u308b\u307e\u3067\u306e\u30b9\u30c6\u30c3\u30d7": 6, "\u3092\u7d30\u304b\u304f\u898b\u3066\u3044\u304d\u307e\u3059": 6, "\u91cf\u5b50\u30d3\u30c3\u30c8\u56de\u8def\u306e\u521d\u671f\u72b6\u614b": 6, "\u306bhadamard\u6f14\u7b97\u3092\u9069\u7528\u3057": 6, "\u4e00\u69d8\u306b\u91cd\u306d\u5408\u308f\u3055\u308c\u305f\u72b6\u614b\u3092\u751f\u6210\u3057\u307e\u3059": 6, "\u3053\u306e\u72b6\u614b\u3092": 6, "\u306e\u72b6\u614b\u3092\u5e7e\u4f55\u5b66\u7684\u306b\u8868\u73fe\u3057\u3066\u307f\u307e\u3057\u3087\u3046": 6, "\u91cd\u306d\u5408\u308f\u305b\u72b6\u614b": 6, "\u3068\u6c42\u3081\u308b\u72b6\u614b": 6, "\u304c\u5f35\u308b2\u6b21\u5143\u5e73\u9762\u3092\u8003\u3048\u307e\u3059": 6, "\u306b\u76f4\u4ea4\u3059\u308b\u72b6\u614b": 6, "perp": 6, "\u3068\u8868\u73fe\u3067\u304d\u308b\u305f\u3081": 6, "\u3053\u306e\u5e73\u9762\u4e0a\u3067\u306f": 6, "\u306b\u76f4\u4ea4\u3059\u308b\u8ef8\u306b\u76f8\u5f53\u3057\u307e\u3059": 6, "\u7c21\u6613\u7684\u306b": 6, "\u3053\u306e\u5e73\u9762\u3067\u306f": 6, "bmatrix": 6, "\u3068\u66f8\u304f\u3053\u3068\u306b\u3057\u307e\u3059": 6, "\u307e\u3068\u3081\u308b\u3068": [6, 8], "\u3053\u306e2\u6b21\u5143\u5e73\u9762\u3067\u306f": 6, "\u3068\u3044\u3046\u4e8c\u3064\u306e\u30d9\u30af\u30c8\u30eb\u306e\u7dda\u5f62\u548c\u3068\u3057\u3066\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 6, "frac1": [6, 16], "\u7b54\u3048\u304c\u4e00\u3064\u3067\u3042\u308b\u305f\u3081": 6, "\u306e\u632f\u5e45\u306f": 6, "\u3092\u5b9a\u7fa9\u3059\u308b\u3068": [6, 16], "arcsin": [6, 12, 19], "\u5e73\u9762\u3067\u306e": 6, "\u3092\u56f3\u793a\u3059\u308b\u3068": 6, "\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059": 6, "\u306b\u30aa\u30e9\u30af\u30eb": 6, "\u3092\u9069\u7528\u3057\u307e\u3059": [6, 16], "\u3053\u306e\u30aa\u30e9\u30af\u30eb\u306f": 6, "\u3068\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059": 6, "\u8ef8\u306b\u95a2\u3057\u3066": 6, "\u3092\u6298\u308a\u8fd4\u3059\u64cd\u4f5c": 6, "\u4e0b\u56f3": [6, 16], "\u306b\u5bfe\u5fdc\u3057\u3066\u304a\u308a": 6, "\u3053\u306e\u64cd\u4f5c\u3067": 6, "\u306e\u4f4d\u76f8\u304c\u53cd\u8ee2\u3057\u307e\u3059": 6, "\u6b21\u306f": 6, "u_0h": 6, "\u306e\u9069\u7528\u3067": 6, "\u3053\u306e\u6f14\u7b97\u306fdiffuser\u3068\u547c\u3070\u308c\u307e\u3059": 6, "equiv": [6, 16, 20], "2h": 6, "diffus": 6, "\u306b\u95a2\u3057\u3066\u6298\u308a\u8fd4\u3059\u64cd\u4f5c\u306b\u5bfe\u5fdc\u3057\u307e\u3059": 6, "u_su_w": 6, "\u3067\u3042\u308b\u305f\u3081": 6, "\u306b\u5411\u3051\u3066\u89d2\u5ea6": 6, "\u3060\u3051\u56de\u8ee2\u3059\u308b\u64cd\u4f5c\u3092\u8868\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059": 6, "\u30921\u56de\u9069\u7528\u3059\u308c\u3070": 6, "\u3060\u3051\u56de\u8ee2\u3059\u308b\u3068\u3044\u3046\u3053\u3068\u306f": 6, "\u56de\u7e70\u308a\u8fd4\u305b\u3070": 6, "\u56de\u8ee2\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059": 6, "\u305d\u306e\u6642\u306e": 6, "\u306e\u72b6\u614b\u306f": 6, "2r": 6, "\u6c42\u3081\u308b\u7b54\u3048": 6, "\u306b\u5230\u9054\u3059\u308b\u305f\u3081\u306b\u306f": 6, "approx": [6, 16], "\u306e\u56de\u6570\u3060\u3051\u56de\u8ee2\u3059\u308c\u3070\u826f\u3044\u3053\u3068\u306b\u306a\u308a\u307e\u3059": 6, "1\u56de\u306e\u56de\u8ee2\u89d2": 6, "\u304c\u5341\u5206\u5c0f\u3055\u3044\u3068\u3057\u3066": 6, "pi4": 6, "cal": [6, 13], "\u306e\u64cd\u4f5c\u3067\u7b54\u3048": 6, "\u306b\u5230\u9054\u3059\u308b\u3053\u3068\u304c\u793a\u305b\u305f\u308f\u3051\u3067\u3042\u308a": 6, "\u53e4\u5178\u8a08\u7b97\u306b\u5bfe\u3059\u308b2\u6b21\u306e\u9ad8\u901f\u5316\u304c\u5f97\u3089\u308c\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3057\u305f": 6, "diffuser\u306e\u5f79\u5272\u3092\u3082\u3046\u5c11\u3057\u898b\u3066\u307f\u307e\u3057\u3087\u3046": 6, "\u3042\u308b\u72b6\u614b": 6, "\u3068\u3044\u3046\u632f\u5e45": 6, "\u306e\u91cd\u306d\u5408\u308f\u305b\u72b6\u614b\u3068\u3057\u3066\u66f8\u304b\u308c\u308b\u3068\u3057\u307e\u3057\u3087\u3046": 6, "\u3053\u306e\u72b6\u614b\u306bdiffuser\u3092\u9069\u7528\u3059\u308b\u3068": 6, "frac2n": 6, "sum_i": [6, 13], "a_i": [6, 21], "\u306f\u632f\u5e45\u306e\u5e73\u5747\u3067\u3059": 6, "\u3053\u306e\u5f0f\u304c\u610f\u5473\u3059\u308b\u3068\u3053\u308d\u306f": 6, "\u306e\u632f\u5e45": 6, "\u5e73\u5747\u306b\u5bfe\u3059\u308b\u6442\u52d5\u306e\u5f62": 6, "\u3067\u8868\u73fe\u3067\u304d\u308b\u3068\u8003\u3048\u308b\u3068\u7406\u89e3\u3057\u3084\u3059\u304f\u306a\u308a\u307e\u3059": 6, "diffuser\u3092\u9069\u7528\u3057\u305f\u5f8c\u306e\u632f\u5e45\u304c": 6, "\u306b\u306a\u308b\u3053\u3068\u304b\u3089": 6, "diffuser\u306f\u5e73\u5747": 6, "\u306b\u95a2\u3057\u3066\u632f\u5e45\u3092\u53cd\u8ee2\u3059\u308b\u64cd\u4f5c\u3092\u8868\u3057\u3066\u3044\u308b\u3068\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u308f\u3051\u3067\u3059": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u632f\u5e45\u304c\u3069\u306e\u3088\u3046\u306b\u5897\u5e45\u3055\u308c\u308b\u306e\u304b": 6, "\u5b9f\u969b\u76ee\u3067\u898b\u3066\u307f\u308b\u3053\u3068\u306b\u3057\u307e\u3059": 6, "\u307e\u305a\u6700\u521d\u306ehadamard\u5909\u63db\u3067": 6, "\u5168\u3066\u306e\u8a08\u7b97\u57fa\u5e95\u304c\u7b49\u3057\u3044\u632f\u5e45\u3092\u6301\u3064\u91cd\u306d\u5408\u308f\u305b\u72b6\u614b\u3092\u751f\u6210\u3057\u307e\u3059": 6, "\u4e0b\u56f3\u306e1": 6, "\u6a2a\u8ef8\u306f": 6, "\u500b\u306e\u8a08\u7b97\u57fa\u5e95": 6, "\u7e26\u8ef8\u306f\u5404\u57fa\u5e95\u306e\u632f\u5e45\u306e\u5927\u304d\u3055\u3092\u8868\u3057\u3066\u304a\u308a": 6, "\u5168\u3066\u306e\u57fa\u5e95\u304c": 6, "\u306e\u5927\u304d\u3055\u306e\u632f\u5e45\u3092\u6301\u3063\u3066\u3044\u307e\u3059": 6, "\u632f\u5e45\u306e\u5e73\u5747\u3092\u8d64\u7834\u7dda\u3067\u8868\u793a": 6, "\u6b21\u306b\u30aa\u30e9\u30af\u30eb": 6, "\u3092\u9069\u7528\u3059\u308b\u3068": 6, "\u306e\u4f4d\u76f8\u304c\u53cd\u8ee2\u3057": 6, "\u632f\u5e45\u304c": 6, "\u4e0b\u56f3\u306e2": 6, "\u3053\u306e\u72b6\u614b\u3067\u306e\u632f\u5e45\u306e\u5e73\u5747\u306f": 6, "\u306b\u306a\u308a": [6, 18], "\u306e\u72b6\u614b\u3088\u308a\u4f4e\u304f\u306a\u308a\u307e\u3059": 6, "\u6700\u5f8c\u306bdiffuser\u3092\u9069\u7528\u3059\u308b\u3068": 6, "\u5e73\u5747\u306b\u95a2\u3057\u3066\u632f\u5e45\u3092\u53cd\u8ee2\u3057\u307e\u3059": 6, "\u4e0b\u56f3\u306e3": 6, "\u305d\u306e\u7d50\u679c": 6, "\u306e\u632f\u5e45\u304c\u5897\u5e45\u3055\u308c": 6, "\u4ee5\u5916\u306e\u57fa\u5e95\u306e\u632f\u5e45\u306f\u6e1b\u5c11\u3057\u307e\u3059": 6, "1\u56de\u306e\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u53cd\u5fa9\u64cd\u4f5c\u3067": 6, "\u306e\u632f\u5e45\u304c\u7d043\u500d\u7a0b\u5ea6\u5897\u5e45\u3059\u308b\u3053\u3068\u3082\u56f3\u304b\u3089\u898b\u3066\u3068\u308c\u307e\u3059": 6, "\u3053\u306e\u64cd\u4f5c\u3092\u7e70\u308a\u8fd4\u3057\u5b9f\u884c\u3059\u308c\u3070": 6, "\u306e\u632f\u5e45\u304c\u3055\u3089\u306b\u5897\u5e45\u3055\u308c\u308b\u305f\u3081": 6, 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"\u3068\u3044\u3046\u30ad\u30fc\u30ef\u30fc\u30c9\u3067\u59cb\u307e\u308b\u30d6\u30ed\u30c3\u30af\u306b\u5165\u308c": 12, "\u76f4\u5f8c\u306b": 12, "\u3068\u3044\u3046\u30ad\u30fc\u30ef\u30fc\u30c9\u304b\u3089\u59cb\u307e\u308b\u30d6\u30ed\u30c3\u30af\u306b\u5bfe\u51e6\u306e\u305f\u3081\u306e\u30b3\u30fc\u30c9\u3092\u8a18\u8ff0\u3057\u3066\u304a\u304d\u307e\u3059": 12, "try\u4ee5\u4e0b\u306e\u30d6\u30ed\u30c3\u30af\u3067\u4f8b\u5916\u304c\u767a\u751f\u3059\u308b\u3068": 12, "\u30d6\u30ed\u30c3\u30af\u4e2d\u306e\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u304c\u305d\u306e\u6642\u70b9\u3067\u6b62\u307e\u308a": 12, "\u5f8c\u308d\u306eexcept\u30d6\u30ed\u30c3\u30af\u304c\u5b9f\u884c\u3055\u308c\u308b": 12, "typeerror": 12, "typeerror\u578b\u306e\u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u3089": 12, "\u4ee5\u4e0b\u306e\u30d6\u30ed\u30c3\u30af\u3092\u5b9f\u884c\u3059\u308b": 12, "oop": 12, "compat": 12, "\u305d\u306e\u4ed6\u306e\u578b\u306e\u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u3089": 12, "unexpect": 12, "got": 12, "ex": 12, "\u4e0a\u306e\u4f8b\u306b\u3042\u308b\u3088\u3046\u306b": 12, "\u4f8b\u5916\u306b\u3082\u578b\u304c\u3042\u308a": 12, "\u4f8b\u5916\u767a\u751f\u306e\u3055\u307e\u3056\u307e\u306a\u7406\u7531\u3092\u533a\u5225\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059": 12, "\u4f8b\u5916\u3068\u305d\u306e\u51e6\u7406\u306fpython\u306e\u6b63\u5e38\u306a\u30d7\u30ed\u30b0\u30e9\u30e0\u30d5\u30ed\u30fc\u306e\u4e00\u90e8\u3068\u3057\u3066\u6349\u3048\u308b\u3079\u304d\u3067": 12, "\u3092\u4e0a\u624b\u306b\u4f7f\u3046\u3053\u3068\u3067\u3088\u308a": 12, "python\u3089\u3057\u3044": 12, "\u30b3\u30fc\u30c9\u304c\u66f8\u3051\u308b\u3068\u8a00\u308f\u308c\u3066\u3044\u307e\u3059": 12, "\u8f9e\u66f8\u578b\u306b\u5bfe\u3057\u3066\u5b58\u5728\u3057\u306a\u3044\u30ad\u30fc\u3067\u5024\u3092\u53c2\u7167\u3057\u3088\u3046\u3068\u3059\u308b\u3068": 12, "keyerror": 12, "\u3053\u308c\u3092\u56de\u907f\u3059\u308b\u306b\u306f\u30ad\u30fc\u304c\u5b58\u5728\u3059\u308b\u304b\u4e8b\u524d\u306b\u78ba\u8a8d\u3059\u308c\u3070\u3044\u3044\u308f\u3051\u3067\u3059\u304c": 12, "\u78ba\u8a8d\u3057\u305f\u7d50\u679c\u30ad\u30fc\u304c\u5b58\u5728\u3057\u305f\u3068\u3057\u3066": 12, "\u305d\u306e\u5f8c\u5024\u3092\u53c2\u7167\u3059\u308b\u3068\u3059\u308b\u3068": 12, "\u7d50\u5c40\u4e8c\u56de\u540c\u3058\u30ad\u30fc\u3092\u691c\u7d22\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059": 12, "\u305d\u308c\u306a\u3089": 12, "\u6700\u521d\u304b\u3089\u53c2\u7167\u3057\u3088\u3046\u3068\u3057\u3066\u307f\u3066": 12, "\u30a8\u30e9\u30fc\u304c\u51fa\u305f\u3089\u30ad\u30fc\u304c\u5b58\u5728\u3057\u306a\u304b\u3063\u305f\u3068\u304d\u306e\u51e6\u7406\u306b\u79fb\u308b": 12, "\u3068\u3044\u3046\u65b9\u304c\u5b9f\u306f\u52b9\u7387\u7684\u3067\u3059": 12, "89": 12, "dictionari": 12, "d\u306b\u30ad\u30fc": 12, "\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u78ba\u8a8d\u3057": 12, "\u305d\u308c\u304b\u3089\u305d\u306e\u5024\u3092\u4f7f\u3046": 12, "\u304c\u306a\u3051\u308c\u3070\u4ed6\u306e\u3053\u3068\u3092\u3059\u308b": 12, "\u30ad\u30fc\u306e\u5b58\u5728\u3092\u8abf\u3079\u308b\u306b\u306fin\u3068\u3044\u3046\u6f14\u7b97\u5b50\u3092\u4f7f\u3046": 12, "a_valu": 12, "doe": 12, "entri": 12, "\u3082\u3063\u3068\u52b9\u7387\u7684\u306a\u5b9f\u88c5": 12, "python\u306e\u6587\u6cd5\u3084\u57fa\u672c\u6982\u5ff5\u3092\u4e00\u901a\u308a\u3055\u3089\u3063\u305f\u306e\u3067": 12, "\u5b9f\u969b\u306b\u30ef\u30fc\u30af\u30d6\u30c3\u30af\u306b\u767b\u5834\u3059\u308b\u3088\u3046\u306a\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u66f8\u304f\u306e\u306b\u5fc5\u8981\u306a\u3082\u306e\u306e\u7d39\u4ecb\u306b\u79fb\u308a\u307e\u3059": 12, "\u7d44\u307f\u8fbc\u307f\u95a2\u6570\u3068\u306f": 12, "\u306a\u3057\u3067python\u8a00\u8a9e\u306b\u6700\u521d\u304b\u3089\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u95a2\u6570\u306e\u3053\u3068\u3067\u3059": 12, "\u6570\u5024\u306e\u7d76\u5bfe\u5024\u3092\u8fd4\u3059": 12, "6j": [12, 17], "\u8907\u7d20\u6570\u306e\u7d76\u5bfe\u5024": 12, "\u914d\u5217\u306e\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3068\u8981\u7d20\u3092\u9806\u756a\u306b\u8fd4\u3059": 12, "\u30eb\u30fc\u30d7\u306e\u7e70\u308a\u8fd4\u3057\u3054\u3068\u306b": 12, "\u30a4\u30f3\u30c7\u30c3\u30af\u30b9": 12, "element\u306f": 12, "\u306b\u306a\u308b": [12, 19], "isinst": 12, "\u5909\u6570\u3084\u30ea\u30c6\u30e9\u30eb\u304c\u7279\u5b9a\u306e\u30af\u30e9\u30b9\u306e\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u304b\u30c1\u30a7\u30c3\u30af\u3059\u308b": 12, "\u914d\u5217\u3084\u8f9e\u66f8\u306e\u9577\u3055\u3092\u8fd4\u3059": 12, "\u5f15\u6570\u306e\u4e2d\u304b\u3089\u6700\u5927\u306e\u3082\u306e\u3092\u8fd4\u3059": 12, "min": [12, 19, 20], "\u5f15\u6570\u306e\u4e2d\u304b\u3089\u6700\u5c0f\u306e\u3082\u306e\u3092\u8fd4\u3059": 12, "\u6bd4\u8f03\u6f14\u7b97\u5b50\u304c\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308b\u578b\u3067\u3042\u308c\u3070\u6210\u7acb": 12, "\u5f15\u6570\u304c\u914d\u5217\u306e\u5834\u5408": 12, "\u8981\u7d20\u304c\u6bd4\u8f03\u3055\u308c\u308b": 12, "\u6587\u5b57\u5217\u3092\u8868\u793a\u3059\u308b": 12, "ve": 12, "alreadi": 12, "seen": 12, "\u4e00\u5b9a\u9593\u9694\u306e\u6574\u6570\u306e\u914d\u5217\u3092\u4f5c\u308b": 12, "78": 12, "78\u304b\u308991\u307e\u3067\u306e14\u500b\u306e\u6574\u6570\u306e\u914d\u5217": 12, "3\u4ee5\u4e0a10\u672a\u6e80": 12, "\u9593\u96942\u306e\u6574\u6570\u306e\u914d\u5217": 12, "\u9593\u9694\u306f\u8ca0\u3067\u3082\u3088\u3044": 12, "\u914d\u5217\u306e\u8981\u7d20\u306e\u7dcf\u548c\u3092\u8fd4\u3059": 12, "\u8907\u6570\u306e\u914d\u5217\u304b\u3089\u8981\u7d20\u3092\u30bf\u30d7\u30eb\u306b\u307e\u3068\u3081\u305f\u914d\u5217\u3092\u4f5c\u308b": 12, "char": 12, "num": 12, "\u306efor\u30eb\u30fc\u30d7\u3068\u7b49\u4fa1": 12, "\u8f9e\u66f8": 12, "\u306a\u3069\u306e\u7d44\u307f\u8fbc\u307f\u578b\u3067\u3088\u304f\u4f7f\u3046\u30e1\u30bd\u30c3\u30c9\u3082\u7d39\u4ecb\u3057\u3066\u304a\u304d\u307e\u3059": 12, "\u6587\u5b57\u5217\u306e\u914d\u5217\u3092\u3064\u306a\u3052\u308b": 12, "appl": 12, "banana": 12, "bonobo": 12, "\u914d\u5217\u306e\u6700\u5f8c\u306b\u8981\u7d20\u3092\u8db3\u3059": 12, "\u914d\u5217\u306e\u4efb\u610f\u306e\u4f4d\u7f6e\u306b\u8981\u7d20\u3092\u633f\u5165\u3059\u308b": 12, "\u6700\u521d\u306e\u5f15\u6570\u304c\u4f4d\u7f6e": 12, "\u6b21\u306e\u5f15\u6570\u304c\u8981\u7d20": 12, "\u8f9e\u66f8\u306e\u30ad\u30fc\u306e\u914d\u5217\u3092\u4f5c\u308b": 12, "key\u304c": 12, "\u306e\u3044\u305a\u308c\u304b\u306e\u5024\u306b\u306a\u308b": 12, "\u30ad\u30fc\u304c\u3069\u306e\u9806\u756a\u3067\u73fe\u308c\u308b\u304b\u306f": 12, "\u4e0d\u78ba\u5b9a": 12, "\u8f9e\u66f8\u306e\u5024\u306e\u914d\u5217\u3092\u4f5c\u308b": 12, "value\u304c3": 12, "98\u306e\u3044\u305a\u308c\u304b\u306e\u5024\u306b\u306a\u308b": 12, "keys\u3068\u540c\u3058\u304f\u9806\u5e8f\u306f\u4e0d\u78ba\u5b9a": 12, "\u8f9e\u66f8\u306e\u30ad\u30fc\u3068\u5024\u306e\u30da\u30a2\u306e\u914d\u5217\u3092\u4f5c\u308b": 12, "\u3068\u540c\u3058\u3088\u3046\u306b\u7279\u5b9a\u306e\u30ad\u30fc\u306b\u5bfe\u5fdc\u3059\u308b\u5024\u3092\u8fd4\u3059\u304c": 12, "\u30ad\u30fc\u304c\u5b58\u5728\u3057\u306a\u3044\u5834\u5408\u306b\u30c7\u30d5\u30a9\u30eb\u30c8\u306e\u8fd4\u308a\u5024\u3092\u8a2d\u5b9a\u3067\u304d\u308b": 12, "default": 12, "\u8f9e\u66f8\u306a\u3069\u3092\u751f\u6210\u3059\u308b\u3068\u304d\u306b\u6642\u3005\u4f7f\u3046\u69cb\u6587\u3092\u7d39\u4ecb\u3057\u307e\u3059": 12, "comprehens": 12, "\u4e00\u884c\u306e\u4e2d\u3067for\u30eb\u30fc\u30d7\u3092\u56de\u3059": 12, "a0": 12, "a1": 12, "a2": 12, "dict\u306f": 12, "\u5024": 12, "\u306e\u30bf\u30d7\u30eb\u306e\u914d\u5217\u304b\u3089\u3082\u751f\u6210\u3067\u304d\u308b": 12, "v": [12, 13, 19], "v0": 12, "v1": 12, "v2": 12, 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"\u306e\u56fa\u6709\u5024\u3092\u4e0a\u306e\u3088\u3046\u306b\u6c7a\u3081\u308b\u306e\u306f": 17, "\u8aad\u307f\u51fa\u3057\u30ec\u30b8\u30b9\u30bf\u306b\u73fe\u308c\u308b\u56fa\u6709\u5024\u306e\u6570\u5024\u304c": 17, "\u30aa\u30fc\u30d0\u30fc\u30d5\u30ed\u30fc": 17, "\u3092\u8d77\u3053\u3055\u306a\u3044\u3088\u3046\u306b\u3059\u308b\u305f\u3081\u3067\u3059": 17, "\u56fa\u6709\u5024\u3092\u6c42\u3081\u308b\u554f\u984c\u306a\u306e\u306b\u56fa\u6709\u5024\u304c\u5177\u4f53\u7684\u306a\u5024\u306b\u306a\u308b\u3088\u3046\u306b\u898f\u683c\u5316\u5b9a\u6570\u3092\u9078\u3076\u3068\u3044\u3046\u306e\u306f\u4e00\u898b\u77db\u76fe\u3057\u3066\u3044\u308b\u3088\u3046\u306b\u805e\u3053\u3048\u307e\u3059": 17, "\u3067\u89e6\u308c\u305f\u3088\u3046\u306b": 17, "\u91cf\u5b50\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u3067\u8868\u73fe\u3055\u308c\u308b\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306f\u5168\u3066\u57fa\u5e95\u6f14\u7b97\u5b50": 17, "\u500b\u3005\u306e\u57fa\u5e95\u6f14\u7b97\u5b50\u306e\u7a4d\u306e\u56fa\u6709\u5024\u306f": 17, "\u304c\u57fa\u5e95\u6f14\u7b97\u5b50\u306e\u7a4d": 17, "sigma_k": 17, "\u3068\u30a8\u30cd\u30eb\u30ae\u30fc\u4fc2\u6570": 17, "\u3068\u5206\u89e3\u3067\u304d\u308b\u3068\u3059\u308c\u3070": 17, "\u306e\u56fa\u6709\u5024\u306e\u7d76\u5bfe\u5024\u306f\u6700\u5927\u3067": 17, "\u5168\u304f\u672a\u77e5\u306e\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306b\u5bfe\u3057\u3066\u3082": 17, "\u307e\u305a\u898f\u683c\u5316\u5b9a\u6570\u3092": 17, "\u3068\u53d6\u308c\u3070\u3044\u3044\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3059": 17, "\u30b9\u30da\u30af\u30c8\u30eb\u63a8\u5b9a\u306e\u7d50\u679c": 17, "\u6700\u5927\u56fa\u6709\u5024\u304c\u3082\u3063\u3068\u5c0f\u3055\u3044\u5024\u3067\u3042\u308c\u3070": 17, "\u898f\u683c\u5316\u5b9a\u6570\u3092\u8abf\u7bc0\u3057\u3066\u518d\u5ea6\u8a08\u7b97\u3092\u884c\u3048\u3070\u3044\u3044\u3060\u3051\u3067\u3059": 17, "\u540c\u3058\u7406\u5c48\u3067\u8aad\u307f\u51fa\u3057\u30ec\u30b8\u30b9\u30bfr\u306e\u30d3\u30c3\u30c8\u6570\u3082\u6c7a\u3081\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 17, "\u4eca\u5ea6\u306f\u975e\u30bc\u30ed\u3067\u6700\u3082\u5c0f\u3055\u3044\u56fa\u6709\u5024\u306e\u7d76\u5bfe\u5024\u3092\u8003\u3048\u307e\u3059": 17, "\u6700\u5c0f\u5024\u306f": 17, "min_": 17, "s_k": 17, "\u4ee5\u4e0a\u3067\u3059": 17, "\u3053\u306e\u5024\u3092\u6c42\u3081\u308b\u306b\u306f\u539f\u7406\u7684\u306b\u306f": 17, "\u306f\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306e\u9805\u6570": 17, "\u901a\u308a\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u8abf\u3079\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u304c": 17, "\u5b9f\u969b\u306e\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306f\u901a\u5e38\u305d\u3053\u307e\u3067\u8907\u96d1\u306a\u5f62\u3092\u3057\u3066\u3044\u306a\u3044\u306e\u3067": 17, "\u3053\u308c\u3082\u96e3\u3057\u304f\u306a\u3044\u3068\u8003\u3048\u3089\u308c\u307e\u3059": 17, "\u30ec\u30b8\u30b9\u30bfr\u306e\u30d3\u30c3\u30c8\u6570": 17, "\u304c\u8aad\u307f\u3060\u305b\u308b\u3088\u3046\u306b\u8a2d\u5b9a\u3057\u307e\u3059": 17, "\u3053\u308c\u3067\u898f\u683c\u5316\u5b9a\u6570\u3068\u8aad\u307f\u51fa\u3057\u30ec\u30b8\u30b9\u30bf\u306e\u5927\u304d\u3055\u304c\u7c21\u5358\u306b\u6c7a\u3081\u3089\u308c\u308b\u3053\u3068\u304c\u308f\u304b\u308a\u307e\u3057\u305f\u304c": 17, "\u554f\u984c\u306f\u6f14\u7b97\u5b50": 17, "\u3092\u4f5c\u7528\u3055\u305b\u308b\u5bfe\u8c61\u306e\u72b6\u614b": 17, "\u7b2c": 17, "\u6e96\u4f4d\u306e\u30a8\u30cd\u30eb\u30ae\u30fc\u56fa\u6709\u5024\u3092\u77e5\u308a\u305f\u3044\u5834\u5408": 17, "\u3067\u306a\u3051\u308c\u3070\u3044\u3051\u307e\u305b\u3093": 17, "\u7279\u6b8a\u306a\u30b1\u30fc\u30b9\u3067\u306f\u30b9\u30da\u30af\u30c8\u30eb\u63a8\u5b9a\u306e\u524d\u304b\u3089\u56fa\u6709\u30d9\u30af\u30c8\u30eb\u306e": 17, "\u5f53\u305f\u308a": 17, "\u304c\u3064\u3044\u3066\u3044\u308b\u3053\u3068\u304c\u3042\u308b\u304b\u3082\u3057\u308c\u307e\u305b\u3093\u304c": 17, "\u5f53\u7136": 17, "\u4e00\u822c\u306e\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306b\u5bfe\u3057\u3066": 17, "\u4efb\u610f\u306e": 17, "\u306b\u3064\u3044\u3066\u305d\u306e\u3088\u3046\u306a\u72b6\u614b\u3092\u6e96\u5099\u3059\u308b\u3053\u3068\u306f\u3067\u304d\u307e\u305b\u3093": 17, "\u7cfb\u306e\u6700\u4f4e\u30a8\u30cd\u30eb\u30ae\u30fc": 17, "theta_0": 17, "\u306e\u624b\u6cd5\u306a\u3069\u3092\u7528\u3044\u3066\u6700\u4f4e\u30a8\u30cd\u30eb\u30ae\u30fc\u72b6\u614b\u306e\u8fd1\u4f3c\u3092\u5b9f\u73fe\u3057": 17, "\u305d\u308c\u3092s\u306e\u521d\u671f\u72b6\u614b\u3068\u3059\u308b\u3053\u3068\u3067": 17, "\u6bd4\u8f03\u7684\u306b\u5b89\u5b9a\u3057\u3066\u6c42\u3081\u308b\u3053\u3068\u306f\u53ef\u80fd\u3067\u3059": 17, "\u4e0a\u306e\u624b\u6cd5\u306f\u539f\u7406\u7684\u306b\u306f\u30b9\u30da\u30af\u30c8\u30eb\u306e\u5b8c\u5168\u306a\u5206\u89e3\u306b\u4f7f\u3048\u307e\u3059\u304c": 17, "\u5b9f\u969b\u306b\u306f\u6700\u4f4e\u30a8\u30cd\u30eb\u30ae\u30fc\u3068\u305d\u306e\u56fa\u6709\u30d9\u30af\u30c8\u30eb\u3092\u6b63\u78ba\u306b\u6c42\u3081\u308b\u305f\u3081\u306b\u5229\u7528\u3059\u308b\u3053\u3068\u304c\u591a\u3044\u3068\u8003\u3048\u3089\u308c\u307e\u3059": 17, "\u4f4d\u76f8\u63a8\u5b9a\u3092\u5229\u7528\u3057\u3066\u30cf\u30a4\u30bc\u30f3\u30d9\u30eb\u30b0\u30e2\u30c7\u30eb\u306e\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306e\u30b9\u30da\u30af\u30c8\u30eb\u3092\u6c42\u3081\u3066\u307f\u307e\u3057\u3087\u3046": 17, "\u3092\u91cf\u5b50\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u4e0a\u3067\u8a08\u7b97\u3059\u308b\u305f\u3081\u306b": 17, "\u30c8\u30ed\u30c3\u30bf\u30fc\u5206\u89e3\u3092\u4f7f\u3044\u307e\u3059": 17, "\u56de\u8ee2\u30b2\u30fc\u30c8\u306e\u5b9f\u88c5\u6cd5\u306f": 17, "\u6b21\u306e\u30bb\u30eb\u3067\u306f": 17, "\u3092\u9234\u6728": 17, "\u30c8\u30ed\u30c3\u30bf\u30fc\u5206\u89e3\u3057\u305f\u91cf\u5b50\u56de\u8def\u3092\u8fd4\u3059\u95a2\u6570\u3092\u5b9a\u7fa9\u3057\u3066\u3044\u307e\u3059": 17, "\u5f15\u6570": 17, "num_step": 17, "\u3067\u5206\u89e3\u306e\u7d30\u304b\u3055\u3092\u6307\u5b9a\u3067\u304d\u307e\u3059": 17, "trotter_twopi_heisenberg": 17, "state_regist": 17, "energy_norm": 17, "heisenberg": 17, "sum_of_sigma": 17, "\u03c9": 17, "neg": 17, "evolut": 17, "\u03c4": 17, "which": 17, "lead": 17, "emploi": 17, "suzuki": 17, "decomposit": 17, "correspond": 17, "perform": 17, "strength": 17, "rel": 17, "constant": [17, 21], "divid": 17, "\u03c9\u03c4": 17, "\u03b4u": 17, "step_siz": 17, "\u6b21\u306e\u30bb\u30eb\u3067\u30b9\u30da\u30af\u30c8\u30eb\u63a8\u5b9a\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3057\u3066\u3044\u307e\u3059": 17, "\u3053\u306e\u95a2\u6570\u306f\u72b6\u614b\u30ec\u30b8\u30b9\u30bf": 17, "\u8aad\u307f\u51fa\u3057\u30ec\u30b8\u30b9\u30bf": 17, "\u6642\u9593\u767a\u5c55\u56de\u8def\u3092\u5f15\u6570\u306b\u53d6\u308a": 17, "\u30b9\u30da\u30af\u30c8\u30eb\u63a8\u5b9a\u306e\u91cf\u5b50\u56de\u8def\u3092\u8fd4\u3057\u307e\u3059": 17, "spectrum_estim": 17, "readout_regist": 17, "u_circuit": 17, "spectrum": 17, "contain": 17, "callabl": 17, "appli": 17, "convert": 17, "controlled_u_g": 17, "specifi": 17, "jtarg": 17, "jctrl": 17, "\u3053\u306e\u554f\u984c\u3067\u306f": 17, "\u4e0a\u3067\u53b3\u5bc6\u89e3\u3092\u6c42\u3081\u305f": 17, "\u306e\u30b1\u30fc\u30b9\u3092\u8abf\u3079\u307e\u3059": 17, "\u4eca\u56de\u306f\u3059\u3067\u306b\u30a8\u30cd\u30eb\u30ae\u30fc\u56fa\u6709\u5024\u3092\u77e5\u3063\u3066\u3044\u308b\u306e\u3067": 17, "\u30cf\u30df\u30eb\u30c8\u30cb\u30a2\u30f3\u306e\u898f\u683c\u5316\u5b9a\u6570\u3092": 17, "16j": 17, "\u8aad\u307f\u51fa\u3057\u30ec\u30b8\u30b9\u30bf\u306e\u7d42\u72b6\u614b\u304c\u5358\u7d14\u306b\u306a\u308b\u3088\u3046\u306b\u3057\u307e\u3059": 17, "\u3053\u306e\u3068\u304d\u8aad\u307f\u51fa\u3057\u306f\u7b26\u53f7\u4ed8\u304d\u3067": 17, "\u6700\u5927\u7d76\u5bfe\u5024\u304c": 17, "\u3068\u3059\u308c\u3070\u30aa\u30fc\u30d0\u30fc\u30d5\u30ed\u30fc\u3092\u56de\u907f\u3067\u304d\u307e\u3059": 17, "\u6b21\u306e\u30bb\u30eb\u3067\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u30b9\u30da\u30af\u30c8\u30eb\u63a8\u5b9a\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8a2d\u5b9a\u3057\u307e\u3059": 17, "normal": [17, 19], "per": 17, "tune": 17, "best": 17, "balanc": 17, "accuraci": 17, "versu": 17, "depth": 17, "n_state": 17, "n_readout": 17, "\u4e0a\u3067\u6b63\u3057\u304f\u95a2\u6570\u3092\u304b\u3051\u3066\u3044\u308b\u304b\u78ba\u8a8d\u3057\u3066\u304a\u304d\u307e\u3057\u3087\u3046": 17, "se_circuit": 17, "\u72b6\u614b\u30ec\u30b8\u30b9\u30bf\u306e\u521d\u671f\u72b6\u614b\u3092": 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"\u30a8\u30e9\u30fc\u306e\u767a\u751f\u7387\u3092\u6975\u529b\u4f4e\u304f\u3057\u3066": 18, "\u30a8\u30e9\u30fc\u304c\u767a\u751f\u3059\u308b\u524d\u306b\u5168\u3066\u306e\u8a08\u7b97\u3092\u7d42\u3048\u308b\u3053\u3068": 18, "nisq\u306e\u5ef6\u9577": 18, "\u4f8b\u3048\u30702048\u30d3\u30c3\u30c8\u306e\u6574\u6570\u306e\u7d20\u56e0\u6570\u5206\u89e3\u3092\u30a8\u30e9\u30fc\u306a\u3057\u3067\u5b8c\u9042\u3059\u308b\u306b\u306f": 18, "\u30a8\u30e9\u30fc\u7387\u304c": 18, "\u3088\u308a\u3082\u306f\u308b\u304b\u306b\u4f4e\u304f\u306a\u3044\u3068\u3044\u3051\u307e\u305b\u3093": 18, "\u73fe\u5728\u306e\u30de\u30b7\u30f3\u306e\u30a8\u30e9\u30fc\u7387\u304c": 18, "\u7a0b\u5ea6\u306a\u306e\u3067": 18, "\u3053\u308c\u306f\u3042\u307e\u308a\u73fe\u5b9f\u7684\u306a\u30b4\u30fc\u30eb\u3067\u306f\u3042\u308a\u307e\u305b\u3093": 18, "\u305d\u3082\u305d\u3082\u30a8\u30e9\u30fc\u306f\u78ba\u7387\u7684\u306b\u8d77\u3053\u308b\u306e\u3067": 18, "\u3069\u3093\u306a\u306b\u77ed\u3044\u56de\u8def\u3067\u3082\u30a8\u30e9\u30fc\u304c\u4e57\u308b\u3053\u3068\u306f\u3042\u308a\u3048\u307e\u3059": 18, "\u96e3\u3057\u305d\u3046\u306b\u898b\u3048\u3066\u4e00\u756a\u5b9f\u73fe\u53ef\u80fd\u6027\u304c\u3042\u308b\u306e\u304c": 18, "\u8a08\u7b97\u9014\u4e2d\u306b\u30a8\u30e9\u30fc\u304c\u8d77\u304d\u305f\u3068\u304d": 18, "\u305d\u308c\u3092\u691c\u77e5\u3057\u3066\u8a02\u6b63\u3059\u308b\u3053\u3068": 18, "\u56de\u8def\u306e\u5b9f\u884c\u4e2d\u306b\u30ea\u30a2\u30eb\u30bf\u30a4\u30e0\u3067\u30a8\u30e9\u30fc\u8a02\u6b63\u304c\u884c\u3048\u308c\u3070": 18, "\u8a02\u6b63\u306e\u305f\u3081\u306e\u69d8\u3005\u306a\u30b3\u30b9\u30c8": 18, "\u8ffd\u52a0\u306e\u91cf\u5b50\u30d3\u30c3\u30c8\u3084\u30b2\u30fc\u30c8": 18, "\u306f\u3042\u308b\u3082\u306e\u306e": 18, "\u8a08\u7b97\u7d50\u679c\u3060\u3051\u3092\u898b\u308c\u3070\u30a8\u30e9\u30fc\u306e\u306a\u3044\u7406\u60f3\u7684\u306a\u91cf\u5b50\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u3092\u4f7f\u3046\u306e\u3068\u540c\u3058\u3082\u306e\u304c\u5f97\u3089\u308c\u307e\u3059": 18, "\u305d\u306e\u3088\u3046\u306a\u91cf\u5b50\u30b3\u30f3\u30d4\u30e5\u30fc\u30bf\u306f": 18, "ftqc": 18, "fault": 18, "toler": 18, "\u3068\u547c\u3070\u308c": 18, "nisq\u3068\u306f\u8cea\u7684\u306b\u7570\u306a\u308b\u3082\u306e\u3068\u307f\u306a\u3055\u308c\u3066\u3044\u307e\u3059": 18, "\u30a8\u30e9\u30fc\u3068\u3044\u3046\u306e\u306f\u30ce\u30a4\u30ba\u306a\u3069\u30b3\u30f3\u30c8\u30ed\u30fc\u30eb\u3067\u304d\u306a\u3044\u8981\u56e0\u3067\u30e9\u30f3\u30c0\u30e0\u306b\u767a\u751f\u3059\u308b\u3082\u306e\u306a\u306e\u3067": 18, "\u91cf\u5b50\u30a8\u30e9\u30fc\u8a02\u6b63\u304c\u53ef\u80fd\u3067\u3042\u308b\u3068\u3044\u3046\u3053\u3068\u81ea\u4f53\u304c\u975e\u81ea\u660e\u3067\u9a5a\u304f\u3079\u304d\u4e8b\u5b9f\u3067\u3059": 18, "\u305d\u306e\u80cc\u666f\u306b\u306f\u91cf\u5b50\u529b\u5b66\u306e\u539f\u7406\u304c\u3042\u308a": 18, "\u3069\u3093\u306a\u30a8\u30e9\u30fc\u3082": 18, "\u91cf\u5b50\u30d3\u30c3\u30c8\u3078\u306e\u5f71\u97ff\u3060\u3051\u3092\u898b\u308c\u3070": 18, "\u6709\u9650\u306e\u91cf\u5b50\u6f14\u7b97\u5b50\u306e\u7d44\u307f\u5408\u308f\u305b\u304c\u304b\u304b\u308b\u306e\u3068\u7b49\u4fa1\u3067\u3042\u308b\u3068\u3044\u3046\u3053\u3068\u304c\u793a\u3055\u308c\u3066\u3044\u307e\u3059": 18, "\u305d\u308c\u3089\u306e\u5f71\u97ff\u3092\u691c\u77e5\u3057\u6253\u3061\u6d88\u3059\u3088\u3046\u306a\u91cf\u5b50\u56de\u8def\u3092\u7d44\u3080\u3053\u3068\u304c\u3067\u304d\u308c\u3070": 18, "\u30a8\u30e9\u30fc\u8a02\u6b63\u304c\u5b9f\u73fe\u3055\u308c\u307e\u3059": 18, "\u91cf\u5b50\u30a8\u30e9\u30fc\u8a02\u6b63\u306b\u3064\u3044\u3066\u306f\u53e4\u304f\u304b\u3089\u7814\u7a76\u304c\u306a\u3055\u308c\u3066\u304a\u308a": 18, "\u3059\u3067\u306b\u8907\u6570\u306e\u65b9\u6cd5\u304c\u77e5\u3089\u308c\u3066\u3044\u307e\u3059": 18, "\u305d\u308c\u3067\u3082\u307e\u3060ftqc\u304c\u5b9f\u73fe\u3055\u308c\u3066\u3044\u306a\u3044\u306e\u306f": 18, "\u30a8\u30e9\u30fc\u8a02\u6b63\u81ea\u4f53\u306b\u591a\u6570\u306e\u91cf\u5b50\u30d3\u30c3\u30c8\u304c\u5fc5\u8981\u3067\u3042\u308b\u4e0a\u306b": 18, "\u7d76\u5bfe\u7684\u306a\u30a8\u30e9\u30fc\u7387\u304c\u4e00\u5b9a\u5024\u4ee5\u4e0b\u3067\u306a\u3051\u308c\u3070\u3044\u3051\u306a\u3044\u304b\u3089\u3067\u3059": 18, "\u30a8\u30e9\u30fc\u304c\u8a02\u6b63\u3067\u304d\u308b\u3068\u8a00\u3063\u3066\u3082": 18, "\u8a02\u6b63\u3059\u308b\u305f\u3081\u306e\u56de\u8def\u81ea\u4f53\u304c\u30a8\u30e9\u30fc\u3067\u30c7\u30bf\u30e9\u30e1\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u3088\u3046\u3067\u306f\u5143\u3082\u5b50\u3082\u306a\u3044\u306e\u3067": 18, "\u3084\u306f\u308a\u3042\u308b\u7a0b\u5ea6\u306e\u5b89\u5b9a\u6027\u304c\u5fc5\u8981\u306a\u306e\u3067\u3059": 18, "\u3057\u304b\u3057\u9006\u306b": 18, "\u305d\u308c\u305e\u308c\u306e\u30a8\u30e9\u30fc\u8a02\u6b63\u30d7\u30ed\u30c8\u30b3\u30eb\u306b\u3064\u3044\u3066": 18, "\u6c42\u3081\u3089\u308c\u308b\u30a8\u30e9\u30fc\u7387\u306e\u4e0a\u9650\u5024\u304c\u5b58\u5728\u3059\u308b\u3053\u3068\u304c\u77e5\u3089\u308c\u3066\u3044\u307e\u3059": 18, "\u30a8\u30e9\u30fc\u7387\u304c\u3069\u3053\u307e\u3067\u3082\u4f4e\u304f\u306a\u3051\u308c\u3070\u3044\u3051\u306a\u3044\u3068\u3044\u3046\u3053\u3068\u306f\u306a\u304f": 18, "\u4e00\u5b9a\u5024\u4ee5\u4e0b\u3067\u3042\u308c\u3070\u30a8\u30e9\u30fc\u8a02\u6b63\u3092\u7e70\u308a\u8fd4\u3057\u3066\u4efb\u610f\u306e\u9577\u3055\u306e\u91cf\u5b50\u56de\u8def\u3092\u5b9f\u884c\u3067\u304d\u308b\u3068\u3044\u3046\u3053\u3068\u3067\u3059": 18, "\u3053\u308c\u3092\u30a8\u30e9\u30fc\u7387\u306e": 18, "\u95be\u5024\u5b9a\u7406": 18, "\u30a8\u30e9\u30fc\u306e\u7a2e\u985e\u304c\u6709\u9650\u3067\u3042\u308b\u3068\u3044\u3046\u4e8b\u5b9f\u3068\u5408\u308f\u305b\u3066": 18, "\u30a8\u30e9\u30fc\u8a02\u6b63\u3068\u3044\u3046\u30a2\u30d7\u30ed\u30fc\u30c1\u306e\u5f37\u529b\u306a\u4e0b\u652f\u3048\u306b\u306a\u3063\u3066\u3044\u307e\u3059": 18, "\u4e00\u822c\u306b\u30a8\u30e9\u30fc\u8a02\u6b63\u3092\u3059\u308b\u306b\u306f": 18, "\u500b\u3005\u306e\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u72b6\u614b\u3092": 18, "\u8907\u6570\u306e\u30a8\u30f3\u30bf\u30f3\u30b0\u30eb\u3057\u305f\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u72b6\u614b\u306b": 18, "\u30a8\u30f3\u30b3\u30fc\u30c9": 18, "\u3057\u307e\u3059": 18, "\u30a8\u30f3\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u306e\u7d50\u679c\u751f\u3058\u308b": 18, "1\u91cf\u5b50\u30d3\u30c3\u30c8\u5206\u306e\u60c5\u5831\u3092\u4fdd\u6301\u3059\u308b\u8907\u6570\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u307e\u3068\u307e\u308a\u3092": 18, "\u8ad6\u7406\u91cf\u5b50\u30d3\u30c3\u30c8": 18, "\u3068\u547c\u3073": 18, "\u8ad6\u7406\u91cf\u5b50\u30d3\u30c3\u30c8\u304b\u3089\u5143\u306e": 18, "\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u72b6\u614b\u3092\u5fa9\u5143\u3059\u308b\u3053\u3068\u3092\u30c7\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3068\u547c\u3073\u307e\u3059": 18, "\u7279\u5b9a\u306e\u30a8\u30e9\u30fc\u306b\u5bfe\u3057\u3066\u8010\u6027\u304c\u3042\u308b": 18, "\u30a8\u30e9\u30fc\u306e\u88dc\u6b63\u3092\u53ef\u80fd\u306b\u3059\u308b": 18, "\u8ad6\u7406\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u7d44\u307f\u65b9\u306e\u3053\u3068\u3092\u30a8\u30e9\u30fc\u8a02\u6b63\u30b3\u30fc\u30c9\u3068\u8a00\u3044\u307e\u3059": 18, "\u8ad6\u7406\u91cf\u5b50\u30d3\u30c3\u30c8\u306b\u5bfe\u3057\u3066\u8ad6\u7406\u30b2\u30fc\u30c8\u3084\u8ad6\u7406\u6e2c\u5b9a\u3092\u8003\u3048\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u306e\u3067": 18, "ftqc\u3067\u306e\u91cf\u5b50\u8a08\u7b97\u306e\u6700\u5c0f\u5358\u4f4d\u306f": 18, "\u4f55\u3089\u304b\u306e\u30a8\u30e9\u30fc\u8a02\u6b63\u30b3\u30fc\u30c9\u3067\u4f5c\u3089\u308c\u305f\u8ad6\u7406\u91cf\u5b50\u30d3\u30c3\u30c8\u3068\u306a\u308b\u3068\u8003\u3048\u3089\u308c\u3066\u3044\u307e\u3059": 18, "\u30a8\u30e9\u30fc\u8a02\u6b63\u306f\u5965\u306e\u6df1\u3044\u7814\u7a76\u9818\u57df\u3067\u3059\u304c": 18, "\u4eca\u56de\u306e\u6f14\u7fd2\u3067\u306f\u672c\u683c\u7684\u306a\u8a71\u306f\u305b\u305a": 18, "\u6bd4\u8f03\u7684\u5358\u7d14\u306a": 18, "\u3068\u3044\u3046\u30a8\u30e9\u30fc\u8a02\u6b63\u30b3\u30fc\u30c9\u306e\u5b9f\u88c5\u3060\u3051\u3092\u8003\u3048\u307e\u3059": 18, "shor\u30b3\u30fc\u30c9\u306f\u7d20\u56e0\u6570\u5206\u89e3\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u767a\u6848\u3057\u305fpet": 18, "shor\u306e\u3082\u3046\u4e00\u3064\u306e\u91cd\u8981\u306a\u767a\u660e\u3067": 18, "\u6700\u521d\u306b\u5b9f\u73fe\u3055\u308c\u308bftqc\u3067\u63a1\u7528\u3055\u308c\u308b\u30d7\u30ed\u30c8\u30b3\u30eb\u306e\u6709\u529b\u5019\u88dc\u3067\u306f\u3042\u308a\u307e\u305b\u3093\u304c": 18, "\u30a8\u30e9\u30fc\u8a02\u6b63\u3068\u3044\u3046\u624b\u6cd5\u3092\u7406\u89e3\u3059\u308b\u306e\u306b\u3068\u3066\u3082\u5f79\u306b\u7acb\u3061\u307e\u3059": 18, "shor\u30b3\u30fc\u30c9\u3084\u305d\u306e\u30d9\u30fc\u30b9\u3068\u306a\u308b\u30d3\u30c3\u30c8\u53cd\u8ee2": 18, "\u4f4d\u76f8\u53cd\u8ee2\u30b3\u30fc\u30c9\u306e\u30c7\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u3067\u6b20\u304b\u305b\u306a\u3044\u306e\u304ctoffoli\u30b2\u30fc\u30c8": 18, "ccx": 18, "toffoli\u30b2\u30fc\u30c8\u306f\u30a8\u30e9\u30fc\u8a02\u6b63\u3060\u3051\u3067\u306a\u304f": 18, "\u591a\u91cd\u5236\u5fa1\u30b2\u30fc\u30c8\u306e\u57fa\u672c\u30d1\u30fc\u30c4\u3068\u306a\u308b\u305f\u3081": 18, "\u3055\u307e\u3056\u307e\u306a\u5834\u9762\u3067\u767b\u5834\u3057\u307e\u3059": 18, "\u307e\u305f\u7406\u8ad6\u7684\u306b\u306f": 18, "toffoli\u30b2\u30fc\u30c8\u3068hadamard\u30b2\u30fc\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u3067\u5168\u3066\u306e\u91cf\u5b50\u6f14\u7b97\u304c\u5b9f\u73fe\u3067\u304d\u308b": 18, "\u666e\u904d\u30b2\u30fc\u30c8\u30bb\u30c3\u30c8\u3068\u306a\u308b": 18, "\u3053\u3068\u304b\u3089": 18, "toffoli\u30b2\u30fc\u30c8\u306f\u7279\u5225\u91cd\u8981\u306a\u5b58\u5728\u3067\u3059": 18, "\u4e00\u898b\u5358\u7d14\u305d\u3046\u306b\u898b\u3048\u308btoffoli\u30b2\u30fc\u30c8\u3067\u3059\u304c": 18, "\u8d85\u4f1d\u5c0e\u91cf\u5b50\u56de\u8def\u306a\u3069\u306e\u5b9f\u6a5f\u3067": 18, 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"cp\u30b2\u30fc\u30c8\u3068\u306f": 18, "2\u91cf\u5b50\u30d3\u30c3\u30c8\u30b2\u30fc\u30c8\u3067": 18, "\u4e8c\u3064\u306e\u91cf\u5b50\u30d3\u30c3\u30c8\u304c\u3068\u3082\u306b": 18, "\u3067\u3042\u308b\u3088\u3046\u306a\u8a08\u7b97\u57fa\u5e95\u306e\u4f4d\u76f8\u3092": 18, "\u524d\u9032\u3055\u305b\u308b\u30b2\u30fc\u30c8\u306e\u3053\u3068\u3067\u3057\u305f": 18, "\u307e\u305a\u5236\u5fa1": 18, "cr_z": 18, "\u304b\u3089\u4f5c\u308b\u3053\u3068\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046": 18, "\u306e\u4f5c\u7528\u306f": 18, "\u5de6\u3092\u5236\u5fa1\u30d3\u30c3\u30c8": 18, "\u53f3\u3092\u6a19\u7684\u30d3\u30c3\u30c8\u3068\u3057\u3066": 18, "\u304c\u4f5c\u308c\u308c\u3070": 18, "\u5236\u5fa1\u30d3\u30c3\u30c8\u306b": 18, "\u3092\u304b\u3051\u308b\u3053\u3068\u3067": 18, "\u306e\u4f4d\u76f8\u3092\u63c3\u3048\u308b\u3053\u3068\u304c\u3067\u304d": 18, 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6, "\u30ea\u30b9\u30c8\u306f": 6, "cdots00": 6, "cdots01": 6, "cdots10": 6, "cdots11": 6, "\u307e\u3067\u306e": [6, 14], "\u500b\u306e\u8981\u7d20": 6, "10\u9032\u6570\u8868\u8a18\u3060\u3068": 6, "\u3092\u542b\u3093\u3067\u3044\u307e\u3059": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u91cd\u8981\u306b\u306a\u308b\u306e\u306f": 6, "\u7279\u5b9a\u306e\u72b6\u614b\u306e\u4f4d\u76f8\u3092\u5909\u3048\u308b\u4f4d\u76f8\u30aa\u30e9\u30af\u30eb\u3067\u3059": 6, "\u3067\u4e0e\u3048\u3089\u308c\u308b\u4f4d\u76f8\u30aa\u30e9\u30af\u30eb\u3092\u8003\u3048\u307e\u3059": 6, "\u3064\u307e\u308a\u3042\u308b\u72b6\u614b": 6, "\u306b\u4f5c\u7528\u3059\u308b\u3068": 6, "\u305d\u306e\u72b6\u614b\u306e\u4f4d\u76f8\u3092\u3042\u308b\u95a2\u6570": 6, "\u3060\u3051\u30b7\u30d5\u30c8\u3055\u305b\u308b\u3088\u3046\u306a\u6f14\u7b97\u3067\u3059": 6, "bigg": [6, 8], "\u306e\u3088\u3046\u306a\u95a2\u6570\u3092\u8003\u3048\u308b\u3068": 6, "\u6c42\u3081\u308b\u89e3": 6, "\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30aa\u30e9\u30af\u30eb": 6, "u_w": 6, "\u3053\u306e\u6642": [6, 16], "\u3068\u8868\u73fe\u3067\u304d\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059": 6, "\u95a2\u6570": [6, 16, 17, 19], "\u3092\u8003\u3048\u308b\u3068": 6, "0\u4ee5\u5916\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30e6\u30cb\u30bf\u30ea\u30fc": 6, "u_0": 6, "\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 6, "\u306b\u306a\u308a\u307e\u3059": [6, 19, 20], "\u30b0\u30ed\u30fc\u30d0\u30fc\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u5b9f\u88c5\u3059\u308b\u91cf\u5b50\u56de\u8def\u306e\u69cb\u9020\u306f": 6, "\u4e0b\u56f3\u306e\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059": 6, "\u91cf\u5b50\u30d3\u30c3\u30c8\u306e\u56de\u8def\u3092": 6, "\u306e\u521d\u671f\u72b6\u614b\u304b\u3089\u51fa\u767a\u3057": 6, "hadamard\u6f14\u7b97\u3092\u9069\u7528\u3057\u3066\u91cd\u306d\u5408\u308f\u305b\u72b6\u614b\u3092\u4f5c\u308a\u307e\u3059": 6, "\u305d\u306e\u5f8c": 6, "\u3068\u66f8\u304b\u308c\u3066\u3044\u308b\u6f14\u7b97\u3092\u7e70\u308a\u8fd4\u3057\u9069\u7528\u3057\u307e\u3059": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u53cd\u5fa9": 6, "\u3068\u3082\u547c\u3070\u308c\u308b\u30e6\u30cb\u30bf\u30ea\u30fc\u6f14\u7b97\u3067": 6, "\u4ee5\u4e0b\u306e\u3088\u3046\u306a4\u3064\u306e\u30b9\u30c6\u30c3\u30d7\u304b\u3089\u69cb\u6210\u3055\u308c\u3066\u3044\u307e\u3059": 6, "\u305d\u308c\u305e\u308c\u4e0a\u3067\u8aac\u660e\u3057\u305f\u89e3": 6, "\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30aa\u30e9\u30af\u30eb\u30680\u4ee5\u5916\u306e\u4f4d\u76f8\u3092\u53cd\u8ee2\u3059\u308b\u30aa\u30e9\u30af\u30eb\u3067\u3059": 6, "\u56de\u8def\u306e\u6700\u521d\u306b\u3042\u308bhadamard\u6f14\u7b97\u3068\u5408\u308f\u305b\u3066": 6, "\u30b0\u30ed\u30fc\u30d0\u30fc\u306e\u53cd\u5fa9\u30921\u56de\u5b9f\u884c\u3059\u308b\u307e\u3067\u306e\u30b9\u30c6\u30c3\u30d7": 6, "\u3092\u7d30\u304b\u304f\u898b\u3066\u3044\u304d\u307e\u3059": 6, "\u91cf\u5b50\u30d3\u30c3\u30c8\u56de\u8def\u306e\u521d\u671f\u72b6\u614b": 6, "\u306bhadamard\u6f14\u7b97\u3092\u9069\u7528\u3057": 6, "\u4e00\u69d8\u306b\u91cd\u306d\u5408\u308f\u3055\u308c\u305f\u72b6\u614b\u3092\u751f\u6210\u3057\u307e\u3059": 6, "\u3053\u306e\u72b6\u614b\u3092": 6, "\u306e\u72b6\u614b\u3092\u5e7e\u4f55\u5b66\u7684\u306b\u8868\u73fe\u3057\u3066\u307f\u307e\u3057\u3087\u3046": 6, "\u91cd\u306d\u5408\u308f\u305b\u72b6\u614b": 6, "\u3068\u6c42\u3081\u308b\u72b6\u614b": 6, "\u304c\u5f35\u308b2\u6b21\u5143\u5e73\u9762\u3092\u8003\u3048\u307e\u3059": 6, "\u306b\u76f4\u4ea4\u3059\u308b\u72b6\u614b": 6, "perp": 6, "\u3068\u8868\u73fe\u3067\u304d\u308b\u305f\u3081": 6, "\u3053\u306e\u5e73\u9762\u4e0a\u3067\u306f": 6, "\u306b\u76f4\u4ea4\u3059\u308b\u8ef8\u306b\u76f8\u5f53\u3057\u307e\u3059": 6, "\u7c21\u6613\u7684\u306b": 6, "\u3053\u306e\u5e73\u9762\u3067\u306f": 6, "bmatrix": 6, "\u3068\u66f8\u304f\u3053\u3068\u306b\u3057\u307e\u3059": 6, "\u307e\u3068\u3081\u308b\u3068": [6, 8], "\u3053\u306e2\u6b21\u5143\u5e73\u9762\u3067\u306f": 6, "\u3068\u3044\u3046\u4e8c\u3064\u306e\u30d9\u30af\u30c8\u30eb\u306e\u7dda\u5f62\u548c\u3068\u3057\u3066\u66f8\u304f\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 6, "frac1": [6, 16], "\u7b54\u3048\u304c\u4e00\u3064\u3067\u3042\u308b\u305f\u3081": 6, "\u306e\u632f\u5e45\u306f": 6, "\u3092\u5b9a\u7fa9\u3059\u308b\u3068": [6, 16], "arcsin": [6, 12, 19], "\u5e73\u9762\u3067\u306e": 6, "\u3092\u56f3\u793a\u3059\u308b\u3068": 6, "\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308a\u307e\u3059": 6, "\u306b\u30aa\u30e9\u30af\u30eb": 6, "\u3092\u9069\u7528\u3057\u307e\u3059": [6, 16], "\u3053\u306e\u30aa\u30e9\u30af\u30eb\u306f": 6, "\u3068\u8868\u73fe\u3059\u308b\u3053\u3068\u304c\u53ef\u80fd\u3067\u3059": 6, "\u8ef8\u306b\u95a2\u3057\u3066": 6, "\u3092\u6298\u308a\u8fd4\u3059\u64cd\u4f5c": 6, "\u4e0b\u56f3": [6, 16], "\u306b\u5bfe\u5fdc\u3057\u3066\u304a\u308a": 6, "\u3053\u306e\u64cd\u4f5c\u3067": 6, "\u306e\u4f4d\u76f8\u304c\u53cd\u8ee2\u3057\u307e\u3059": 6, "\u6b21\u306f": 6, "u_0h": 6, "\u306e\u9069\u7528\u3067": 6, "\u3053\u306e\u6f14\u7b97\u306fdiffuser\u3068\u547c\u3070\u308c\u307e\u3059": 6, "equiv": [6, 16, 20], "2h": 6, "diffus": 6, "\u306b\u95a2\u3057\u3066\u6298\u308a\u8fd4\u3059\u64cd\u4f5c\u306b\u5bfe\u5fdc\u3057\u307e\u3059": 6, "u_su_w": 6, "\u3067\u3042\u308b\u305f\u3081": 6, "\u306b\u5411\u3051\u3066\u89d2\u5ea6": 6, "\u3060\u3051\u56de\u8ee2\u3059\u308b\u64cd\u4f5c\u3092\u8868\u3057\u3066\u3044\u308b\u3053\u3068\u304c\u5206\u304b\u308a\u307e\u3059": 6, "\u30921\u56de\u9069\u7528\u3059\u308c\u3070": 6, "\u3060\u3051\u56de\u8ee2\u3059\u308b\u3068\u3044\u3046\u3053\u3068\u306f": 6, "\u56de\u7e70\u308a\u8fd4\u305b\u3070": 6, "\u56de\u8ee2\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059": 6, "\u305d\u306e\u6642\u306e": 6, "\u306e\u72b6\u614b\u306f": 6, "2r": 6, "\u6c42\u3081\u308b\u7b54\u3048": 6, "\u306b\u5230\u9054\u3059\u308b\u305f\u3081\u306b\u306f": 6, 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"\u66f8\u304d\u305f\u3044\u30c6\u30ad\u30b9\u30c8\u304c\u6539\u884c\u3092\u542b\u3080\u5834\u5408\u306f\u6539\u884c\u6587\u5b57": 12, "backslash": 12, "\u3092\u633f\u5165\u3057\u307e\u3059": 12, "ntwo": 12, "\u3053\u308c\u3082\u307e\u305f\u8aad\u307f\u306b\u304f\u3044\u306e\u3067": 12, "\u5b9f\u306fsingl": 12, "quotes\u3068doubl": 12, "quotes\u4ee5\u5916\u306b\u305d\u308c\u3089\u306e\u8a18\u53f7\u30923\u3064\u4e26\u3079\u305f": 12, "\u4e09\u91cd\u30af\u30aa\u30fc\u30c8": 12, "\u3067\u30c6\u30ad\u30b9\u30c8\u3092\u56f2\u307f": 12, "\u6539\u884c\u3092\u542b\u3080\u6587\u5b57\u5217\u3092\u305d\u306e\u307e\u307e\u66f8\u304f\u3053\u3068\u3082\u3067\u304d\u307e\u3059": 12, "long": 12, "\u6539\u884c\u3084\u30af\u30aa\u30fc\u30c6\u30fc\u30b7\u30e7\u30f3\u4ee5\u5916\u306b\u3082": 12, "\u7279\u6b8a\u6587\u5b57": 12, "\u5168\u3066\u30d0\u30c3\u30af\u30b9\u30e9\u30c3\u30b7\u30e5\u3067\u59cb\u307e\u308a\u307e\u3059": 12, 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"\u3053\u308c\u3082\u8aad\u307f\u306b\u304f\u3044\u30c6\u30ad\u30b9\u30c8\u306b\u306a\u3063\u3066\u3057\u307e\u3046\u306e\u3067": 12, "\u30d0\u30c3\u30af\u30b9\u30e9\u30c3\u30b7\u30e5\u304c\u305f\u304f\u3055\u3093\u4f7f\u308f\u308c\u308b\u6587\u5b57\u5217\u3092\u66f8\u304f\u5834\u5408\u306f": 12, "\u3068\u3044\u3046\u30d7\u30ec\u30d5\u30a3\u30c3\u30af\u30b9\u3092\u30af\u30aa\u30fc\u30c6\u30fc\u30b7\u30e7\u30f3\u306e\u524d\u306b\u304f\u3063\u3064\u3051\u307e\u3059": 12, "\u306e\u3064\u3044\u305f\u6587\u5b57\u5217\u4e2d\u3067\u306f": 12, "\u30d0\u30c3\u30af\u30b9\u30e9\u30c3\u30b7\u30e5\u304c\u7279\u6b8a\u6587\u5b57\u3068\u3057\u3066\u89e3\u91c8\u3055\u308c\u307e\u305b\u3093": 12, "\u3068\u304d\u3069\u304d": 12, "\u6587\u5b57\u5217\u578b\u4ee5\u5916\u306e\u5909\u6570\u3092\u6587\u5b57\u5217\u306b\u5909\u63db\u3059\u308b\u5fc5\u8981\u304c\u51fa\u3066\u304d\u307e\u3059": 12, "\u3068\u3044\u3046\u6587\u5b57\u5217\u306e\u5f8c\u306b": 12, 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"attr_x": 12, "_y": 12, "\u30e1\u30bd\u30c3\u30c9\u306e\u4e00\u4f8b": 12, "add_y_to_x": 12, "morefunctionalclass\u306e\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u3092\u4f5c\u308a": 12, "\u5909\u6570m\u306b\u4ee3\u5165\u3059\u308b": 12, "\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e\u5c5e\u6027\u3092\u53c2\u7167\u3059\u308b": 12, "\u30a4\u30f3\u30b9\u30bf\u30f3\u30b9\u306e\u30e1\u30bd\u30c3\u30c9\u3092\u5b9f\u884c\u3059\u308b": 12, "\u5f15\u6570self\u306f\u81ea\u52d5\u3067\u6e21\u3055\u308c\u308b": 12, "call": [12, 19], "python\u30d7\u30ed\u30b0\u30e9\u30e0\u306f\u4e00\u3064\u306e\u30d5\u30a1\u30a4\u30eb\u306b\u66f8\u304d\u304d\u3089\u308c\u307e\u305b\u3093": 12, "\u30b3\u30fc\u30c9\u304c\u9577\u304f\u306a\u308a\u3059\u304e\u308b\u3060\u3051\u3067\u306a\u304f": 12, "\u5b9a\u7fa9\u3055\u308c\u308b\u95a2\u6570\u3084\u30af\u30e9\u30b9\u304c\u4ed6\u306e\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u3082\u5229\u7528\u3067\u304d\u308b\u5834\u5408\u304c\u3042\u308b\u304b\u3089\u3067\u3059": 12, "\u5225\u30d5\u30a1\u30a4\u30eb\u3067\u5b9a\u7fa9\u3055\u308c\u305f\u5909\u6570": 12, "\u30af\u30e9\u30b9\u306a\u3069\u3092\u30d7\u30ed\u30b0\u30e9\u30e0\u4e2d\u3067\u4f7f\u7528\u3059\u308b\u306b\u306f": 12, "\u3068\u3044\u3046\u547d\u4ee4\u3092\u4f7f\u3044": 12, "\u30d5\u30a1\u30a4\u30eb\u306e\u540d\u524d\u3084": 12, "\u5834\u5408\u306b\u3088\u3063\u3066\u306f\u30d5\u30a1\u30a4\u30eb\u4e2d\u306e\u4f7f\u7528\u3057\u305f\u3044\u7279\u5b9a\u306e\u95a2\u6570\u540d\u306a\u3069\u3092\u6307\u5b9a\u3057\u307e\u3059": 12, "\u3067\u53d6\u308a\u8fbc\u307e\u308c\u308b\u30d5\u30a1\u30a4\u30eb\u3092python\u30e2\u30b8\u30e5\u30fc\u30eb\u3068\u547c\u3073\u307e\u3059": 12, "os\u3068\u3044\u3046\u30b9\u30bf\u30f3\u30c0\u30fc\u30c9\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8": 12, "os": 12, "os\u30e2\u30b8\u30e5\u30fc\u30eb\u3067\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u308bgetcwd": 12, "\u73fe\u5728\u3069\u306e\u30c7\u30a3\u30ec\u30af\u30c8\u30ea\u304b\u3089\u30d7\u30ed\u30b0\u30e9\u30e0\u3092\u5b9f\u884c\u3057\u3066\u3044\u308b\u304b\u3092\u8fd4\u3059": 12, "\u3092\u547c\u3076": 12, "getcwd": 12, "ibm\u306e\u91cf\u5b50\u30b3\u30f3\u30d4\u30e5\u30fc\u30c6\u30a3\u30f3\u30b0\u7528\u30e9\u30a4\u30d6\u30e9\u30ea": 12, "\u304b\u3089quantumcircuit\u30af\u30e9\u30b9\u3092\u30a4\u30f3\u30dd\u30fc\u30c8": 12, "\u6570\u5024\u8a08\u7b97\u7528\u30e9\u30a4\u30d6\u30e9\u30ea": 12, "\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3057": 12, "\u30d7\u30ed\u30b0\u30e9\u30e0\u4e2dnp\u3068\u3044\u3046\u77ed\u7e2e\u3057\u305f\u30e2\u30b8\u30e5\u30fc\u30eb\u540d\u3067\u4f7f\u7528": 12, "python\u306e\u975e\u5e38\u306b\u5927\u304d\u306a\u5f37\u307f\u304c": 12, "\u30e6\u30fc\u30b6\u30fc\u30d9\u30fc\u30b9\u306e\u5e83\u3055\u3068\u30aa\u30fc\u30d7\u30f3\u30bd\u30fc\u30b9\u306e\u30e9\u30a4\u30d6\u30e9\u30ea\u306e\u591a\u3055\u3067\u3059": 12, "python\u3067\u3084\u308a\u305f\u3044\u3068\u601d\u3046\u3053\u3068\u306f\u5927\u4f53\u8ab0\u304b\u304c\u3059\u3067\u306b\u5b9f\u88c5\u3057\u3066\u3044\u3066": 12, "pypi": 12, "\u306a\u3069\u3067\u30d1\u30c3\u30b1\u30fc\u30b8\u3068\u3057\u3066\u516c\u958b\u3057\u3066\u3044\u308b\u306e\u3067": 12, "\u30e6\u30fc\u30b6\u30fc\u306f\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3057\u3066\u30d7\u30ed\u30b0\u30e9\u30e0\u4e2d\u304b\u3089\u30e2\u30b8\u30e5\u30fc\u30eb\u3092": 12, "\u3059\u308c\u3070\u3044\u3044": 12, "\u30d7\u30ed\u30b0\u30e9\u30e0\u3067\u60f3\u5b9a\u3057\u3066\u3044\u306a\u3044\u64cd\u4f5c\u304c\u8981\u6c42\u3055\u308c\u305f\u3068\u304d\u306b\u306f": 12, "\u304c\u767a\u751f\u3057\u307e\u3059": 12, "\u4e0a\u306e\u4f8b\u3067\u306f": [12, 19], "\u6587\u5b57\u5217\u3068\u6574\u6570\u3092\u52a0\u3048\u3088\u3046\u3068\u3057\u305f\u3068\u304d\u3084": 12, "if\u6587\u306e\u5206\u5c90\u306b\u3088\u3063\u3066\u5b9a\u7fa9\u3055\u308c\u3066\u3044\u306a\u3044\u5909\u6570\u3092\u53c2\u7167\u3057\u3088\u3046\u3068\u3057\u305f\u3068\u304d\u306b\u4f8b\u5916\u304c\u767a\u751f\u3057\u307e\u3057\u305f": 12, "\u4f8b\u5916\u304c\u767a\u751f\u3059\u308b\u3068\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u5b9f\u884c\u304c\u4e2d\u65ad\u3055\u308c\u307e\u3059\u304c": 12, "\u3053\u308c\u306f\u5b9f\u306f\u5fc5\u305a\u3057\u3082": 12, "\u30af\u30e9\u30c3\u30b7\u30e5": 12, "\u3068\u3044\u3046\u3053\u3068\u3067\u306f\u3042\u308a\u307e\u305b\u3093": 12, "python\u306b\u306f\u4f8b\u5916\u306e\u767a\u751f\u3092\u691c\u77e5\u3057\u3066\u5bfe\u51e6\u3067\u304d\u308b\u4ed5\u7d44\u307f\u304c\u5099\u308f\u3063\u3066\u3044\u307e\u3059": 12, "\u4f8b\u5916\u51e6\u7406\u3092\u3059\u308b\u306b\u306f": 12, "\u4f8b\u5916\u304c\u767a\u751f\u3059\u308b\u53ef\u80fd\u6027\u306e\u3042\u308b\u30b3\u30fc\u30c9\u3092": 12, "\u3068\u3044\u3046\u30ad\u30fc\u30ef\u30fc\u30c9\u3067\u59cb\u307e\u308b\u30d6\u30ed\u30c3\u30af\u306b\u5165\u308c": 12, "\u76f4\u5f8c\u306b": 12, "\u3068\u3044\u3046\u30ad\u30fc\u30ef\u30fc\u30c9\u304b\u3089\u59cb\u307e\u308b\u30d6\u30ed\u30c3\u30af\u306b\u5bfe\u51e6\u306e\u305f\u3081\u306e\u30b3\u30fc\u30c9\u3092\u8a18\u8ff0\u3057\u3066\u304a\u304d\u307e\u3059": 12, "try\u4ee5\u4e0b\u306e\u30d6\u30ed\u30c3\u30af\u3067\u4f8b\u5916\u304c\u767a\u751f\u3059\u308b\u3068": 12, "\u30d6\u30ed\u30c3\u30af\u4e2d\u306e\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u304c\u305d\u306e\u6642\u70b9\u3067\u6b62\u307e\u308a": 12, "\u5f8c\u308d\u306eexcept\u30d6\u30ed\u30c3\u30af\u304c\u5b9f\u884c\u3055\u308c\u308b": 12, "typeerror": 12, "typeerror\u578b\u306e\u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u3089": 12, "\u4ee5\u4e0b\u306e\u30d6\u30ed\u30c3\u30af\u3092\u5b9f\u884c\u3059\u308b": 12, "oop": 12, "compat": 12, "\u305d\u306e\u4ed6\u306e\u578b\u306e\u4f8b\u5916\u304c\u767a\u751f\u3057\u305f\u3089": 12, "unexpect": 12, "got": 12, "ex": 12, "\u4e0a\u306e\u4f8b\u306b\u3042\u308b\u3088\u3046\u306b": 12, "\u4f8b\u5916\u306b\u3082\u578b\u304c\u3042\u308a": 12, "\u4f8b\u5916\u767a\u751f\u306e\u3055\u307e\u3056\u307e\u306a\u7406\u7531\u3092\u533a\u5225\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u3063\u3066\u3044\u307e\u3059": 12, "\u4f8b\u5916\u3068\u305d\u306e\u51e6\u7406\u306fpython\u306e\u6b63\u5e38\u306a\u30d7\u30ed\u30b0\u30e9\u30e0\u30d5\u30ed\u30fc\u306e\u4e00\u90e8\u3068\u3057\u3066\u6349\u3048\u308b\u3079\u304d\u3067": 12, "\u3092\u4e0a\u624b\u306b\u4f7f\u3046\u3053\u3068\u3067\u3088\u308a": 12, "python\u3089\u3057\u3044": 12, "\u30b3\u30fc\u30c9\u304c\u66f8\u3051\u308b\u3068\u8a00\u308f\u308c\u3066\u3044\u307e\u3059": 12, "\u8f9e\u66f8\u578b\u306b\u5bfe\u3057\u3066\u5b58\u5728\u3057\u306a\u3044\u30ad\u30fc\u3067\u5024\u3092\u53c2\u7167\u3057\u3088\u3046\u3068\u3059\u308b\u3068": 12, "keyerror": 12, "\u3053\u308c\u3092\u56de\u907f\u3059\u308b\u306b\u306f\u30ad\u30fc\u304c\u5b58\u5728\u3059\u308b\u304b\u4e8b\u524d\u306b\u78ba\u8a8d\u3059\u308c\u3070\u3044\u3044\u308f\u3051\u3067\u3059\u304c": 12, "\u78ba\u8a8d\u3057\u305f\u7d50\u679c\u30ad\u30fc\u304c\u5b58\u5728\u3057\u305f\u3068\u3057\u3066": 12, "\u305d\u306e\u5f8c\u5024\u3092\u53c2\u7167\u3059\u308b\u3068\u3059\u308b\u3068": 12, "\u7d50\u5c40\u4e8c\u56de\u540c\u3058\u30ad\u30fc\u3092\u691c\u7d22\u3059\u308b\u3053\u3068\u306b\u306a\u308a\u307e\u3059": 12, "\u305d\u308c\u306a\u3089": 12, "\u6700\u521d\u304b\u3089\u53c2\u7167\u3057\u3088\u3046\u3068\u3057\u3066\u307f\u3066": 12, "\u30a8\u30e9\u30fc\u304c\u51fa\u305f\u3089\u30ad\u30fc\u304c\u5b58\u5728\u3057\u306a\u304b\u3063\u305f\u3068\u304d\u306e\u51e6\u7406\u306b\u79fb\u308b": 12, "\u3068\u3044\u3046\u65b9\u304c\u5b9f\u306f\u52b9\u7387\u7684\u3067\u3059": 12, "89": 12, "dictionari": 12, "d\u306b\u30ad\u30fc": 12, "\u304c\u3042\u308b\u304b\u3069\u3046\u304b\u78ba\u8a8d\u3057": 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14, "1\u304c\u5f97\u3089\u308c\u308b\u78ba\u7387\u3092": 14, "\u3044\u3063\u307d\u3046": 14, "\u7d42\u72b6\u614b\u306f": 14, "\u30a2\u30c0\u30de\u30fc\u30eb\u30c6\u30b9\u30c8\u3092\u5229\u7528\u3059\u308c\u3070": 14, "\u304b\u3089\u4f5c\u308b\u56de\u8def": 14, "\u304c\u65e2\u77e5\u306e\u3068\u304d": 14, "\u306e\u8a08\u7b97\u57fa\u5e95\u3067\u306e\u5c55\u958b": 14, "\u3092\u632f\u5e45\u306e\u4f4d\u76f8\u60c5\u5831\u3082\u542b\u3081\u3066\u63a8\u5b9a\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 14, "\u4e0a\u3067\u30c7\u30fc\u30bf\u30ec\u30b8\u30b9\u30bf\u306e\u521d\u671f\u72b6\u614b\u3092": 14, "u_k": 14, "\u3092\u4f5c\u308b\u56de\u8def\u3067\u3059": 14, "\u3068\u3067\u30a2\u30c0\u30de\u30fc\u30eb\u30c6\u30b9\u30c8\u3092\u3059\u308b\u3053\u3068\u3067": 14, "\u306e\u5b9f\u90e8\u3068\u865a\u90e8\u304c\u305d\u308c\u305e\u308c\u8a08\u7b97\u3067\u304d\u307e\u3059": 14, "\u6700\u521d\u306e\u7b49\u53f7\u304c\u6210\u7acb\u3059\u308b\u8a3c\u660e\u306f": 14, "\u5b9f\u7fd2\u306e": 14, "\u9006\u56de\u8def\u306e\u5229\u7528": 14, "\u3053\u308c\u3092": 14, "\u306b\u3064\u3044\u3066\u7e70\u308a\u8fd4\u305b\u3070": 14, "\u3092\u5b8c\u5168\u306b\u6c42\u3081\u3089\u308c\u307e\u3059": 14, "\u4ee5\u4e0b\u3067": [14, 19], "\u65e2\u77e5\u3060\u3051\u3069\u4f55\u304b\u8907\u96d1\u306a\u72b6\u614b": 14, "\u306e\u72b6\u614b\u30d9\u30af\u30c8\u30eb\u3092\u8abf\u3079\u3066\u307f\u307e\u3057\u3087\u3046": 14, "\u3092\u5b9a\u7fa9\u3057\u307e\u3059": 14, "\u30c7\u30fc\u30bf\u30ec\u30b8\u30b9\u30bf\u306e\u30d3\u30c3\u30c8\u6570": 14, "upsi": 14, "iq": [14, 17, 19, 20], "\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3067\u8868\u3055\u308c\u308b\u91cf\u5b50\u56de\u8def\u3092": 14, "\u30e1\u30bd\u30c3\u30c9\u3067\u4e00\u3064\u306e\u30b2\u30fc\u30c8\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u5909\u63db\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 14, "\u3055\u3089\u306b\u305d\u306e\u30b2\u30fc\u30c8\u306b\u5bfe\u3057\u3066": 14, "\u30e1\u30bd\u30c3\u30c9\u3092\u7528\u3044\u308b\u3068": 14, "\u5143\u306e\u56de\u8def\u3092n\u91cf\u5b50\u30d3\u30c3\u30c8\u3067\u5236\u5fa1\u3059\u308b\u5236\u5fa1\u30b2\u30fc\u30c8\u3092\u4f5c\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059": 14, "upsi_g": 14, "cupsi_g": 14, "\u3068\u305d\u306e\u5236\u5fa1\u30b2\u30fc\u30c8\u5316\u306f": 14, "\u306e\u95a2\u6570\u3068\u3057\u3066\u5b9a\u7fa9\u3057\u3066\u304a\u304d\u307e\u3059": 14, "make_cukinv_g": 14, "uk": 14, "k\u306e\uff12\u9032\u6570\u8868\u73fe\u3092\u5f97\u308b\u305f\u3081\u306b": 14, "k_bit": 14, "k_bits\u30a2\u30ec\u30a4\u306e\u3046\u3061": 14, "\u30d3\u30c3\u30c8\u304c\u7acb\u3063\u3066\u3044\u308b\u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u5f97\u3066": 14, "\u305d\u308c\u3089\u306bx\u30b2\u30fc\u30c8\u3092\u304b\u3051\u308b": 14, "\u5f62\u5f0f\u4e0a\u9006\u56de\u8def\u3092\u4f5c\u308b\u304c": 14, "x\u306e\u9006\u64cd\u4f5c\u306fx\u306a\u306e\u3067": 14, "\u5b9f\u306f\u5168\u304f\u540c\u4e00\u306e\u56de\u8def": 14, "ukinv": 14, "ukinv_g": 14, "cukinv_g": 14, 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"\u5168\u3066\u306e\u6574\u6570": 14, "\u306b\u5bfe\u5fdc\u3059\u308b\u30d3\u30c3\u30c8\u5217\u304c\u7b49\u3057\u3044\u78ba\u7387\u3067\u73fe\u308c\u307e\u3059": 14, "\u6e2c\u5b9a\u3067\u30d3\u30c3\u30c8\u5217\u304c\u73fe\u308c\u308b\u78ba\u7387\u306f\u305d\u306e\u30d3\u30c3\u30c8\u5217\u306b\u5bfe\u5fdc\u3059\u308b\u8a08\u7b97\u57fa\u5e95\u306e\u632f\u5e45\u306e\u7d76\u5bfe\u5024\u81ea\u4e57\u3067\u6c7a\u307e\u308b\u306e\u3067": 14, "\u91cd\u306d\u5408\u308f\u305b\u306b\u304a\u3044\u3066\u3042\u308b\u6574\u6570": 14, "\u306e\u7b26\u53f7\u3060\u3051\u9006\u8ee2\u3057\u3066\u3044\u308b\u4ee5\u4e0b\u306e\u72b6\u614b\u3067\u3082\u3084\u306f\u308a\u5168\u3066\u306e\u6574\u6570\u304c\u78ba\u7387": 14, "\u5168\u3066\u306e\u8a08\u7b97\u57fa\u5e95\u306b\u30d0\u30e9\u30d0\u30e9\u306b\u4f4d\u76f8\u56e0\u5b50": 14, "\u304c\u304b\u304b\u3063\u3066\u3044\u3066\u3082\u78ba\u7387\u306f\u540c\u3058\u3067\u3059": 14, "\u5f8c\u306e\u5b9f\u7fd2": [14, 17], "\u3067\u767b\u5834\u3059\u308b\u30b0\u30ed\u30fc\u30d0\u30fc\u63a2\u7d22\u3068\u3044\u3046\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306f": 14, "\u4e0a\u306e\u3088\u3046\u306b\u4e00\u3064\u306e\u8a08\u7b97\u57fa\u5e95\u306e\u7b26\u53f7\u3092\u9006\u8ee2\u3055\u305b\u308b\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u6f14\u7b97\u5b50": 14, "\u3069\u306e\u57fa\u5e95\u304b\u306f\u4e8b\u524d\u306b\u77e5\u3089\u308c\u3066\u3044\u306a\u3044": 14, "\u304c\u4e0e\u3048\u3089\u308c\u305f\u3068\u304d\u306b": 14, "\u7b26\u53f7\u306e\u53cd\u8ee2\u304c\u8d77\u3053\u3063\u3066\u3044\u308b\u8a08\u7b97\u57fa\u5e95\u3092\u52b9\u7387\u3088\u304f\u898b\u3064\u3051\u51fa\u3059\u305f\u3081\u306e\u624b\u6cd5\u3067\u3059": 14, "\u30b0\u30ed\u30fc\u30d0\u30fc\u63a2\u7d22\u3092\u5229\u7528\u3059\u308b\u3068": 14, "\u500b\u306e\u30a8\u30f3\u30c8\u30ea\u30fc\u306e\u3042\u308b\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u304b\u3089\u7279\u5b9a\u306e\u30a8\u30f3\u30c8\u30ea\u30fc\u3092\u63a2\u3057\u51fa\u3059\u306e\u306b": 14, "\u56de\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u3092\u53c2\u7167\u3059\u308c\u3070\u3044\u3044\u3068\u3044\u3046\u3053\u3068\u304c\u308f\u304b\u3063\u3066\u3044\u307e\u3059": 14, "\u4eca\u304b\u3089\u8003\u3048\u308b\u306e\u306f\u305d\u306e\u3088\u3046\u306a\u52b9\u7387\u7684\u306a\u65b9\u6cd5\u3067\u306f\u306a\u304f": 14, "\u540c\u3058\u3088\u3046\u306b\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u6f14\u7b97\u5b50\u304c\u4e0e\u3048\u3089\u308c\u305f\u3068\u304d\u306b": 14, "\u539f\u7406\u7684\u306b\u306f\u7b26\u53f7\u306e\u53cd\u8ee2\u304c\u8d77\u3053\u3063\u3066\u3044\u308b\u57fa\u5e95\u3092\u898b\u3064\u3051\u308b\u3053\u3068\u304c\u3067\u304d\u308b": 14, "\u3068\u3044\u3046\u624b\u6cd5\u3067\u3059": 14, "\u305d\u306e\u305f\u3081\u306b\u632f\u5e45\u306e\u5e72\u6e09\u3092\u5229\u7528\u3057\u307e\u3059": 14, "\u307e\u305a\u306f\u5177\u4f53\u6027\u306e\u305f\u3081\u306b": 14, "\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u306f": 14, 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"\u591a\u91cd\u5236\u5fa1\u30b2\u30fc\u30c8\u306e\u57fa\u672c\u30d1\u30fc\u30c4\u3068\u306a\u308b\u305f\u3081": 18, "\u3055\u307e\u3056\u307e\u306a\u5834\u9762\u3067\u767b\u5834\u3057\u307e\u3059": 18, "\u307e\u305f\u7406\u8ad6\u7684\u306b\u306f": 18, "toffoli\u30b2\u30fc\u30c8\u3068hadamard\u30b2\u30fc\u30c8\u306e\u7d44\u307f\u5408\u308f\u305b\u3067\u5168\u3066\u306e\u91cf\u5b50\u6f14\u7b97\u304c\u5b9f\u73fe\u3067\u304d\u308b": 18, "\u666e\u904d\u30b2\u30fc\u30c8\u30bb\u30c3\u30c8\u3068\u306a\u308b": 18, "\u3053\u3068\u304b\u3089": 18, "toffoli\u30b2\u30fc\u30c8\u306f\u7279\u5225\u91cd\u8981\u306a\u5b58\u5728\u3067\u3059": 18, "\u4e00\u898b\u5358\u7d14\u305d\u3046\u306b\u898b\u3048\u308btoffoli\u30b2\u30fc\u30c8\u3067\u3059\u304c": 18, "\u8d85\u4f1d\u5c0e\u91cf\u5b50\u56de\u8def\u306a\u3069\u306e\u5b9f\u6a5f\u3067": 18, "\u57fa\u672c\u30b2\u30fc\u30c8\u304c1\u91cf\u5b50\u30d3\u30c3\u30c8\u30b2\u30fc\u30c8\u30682\u91cf\u5b50\u30d3\u30c3\u30c8\u30b2\u30fc\u30c8\u306e\u307f\u304b\u3089\u306a\u308b": 18, "ibm\u306e\u30b1\u30fc\u30b9\u3067\u306f": 18, "\u5834\u5408": [18, 19], "\u5b9f\u88c5": 18, "\u57fa\u672c\u30b2\u30fc\u30c8\u3078\u306e\u5206\u89e3": 18, "\u306f\u975e\u81ea\u660e\u3067\u3059": 18, "\u3053\u308c\u304b\u3089": 18, "toffoli\u30b2\u30fc\u30c8\u306e\u4e00\u3064\u306e\u5206\u89e3\u65b9\u6cd5\u3092\u30b9\u30c6\u30c3\u30d7\u3054\u3068\u306b\u5c0e\u3044\u3066\u307f\u307e\u3057\u3087\u3046": 18, "\u307e\u305a\u306f\u5fc5\u8981\u306a\u30e2\u30b8\u30e5\u30fc\u30eb\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b": 18, "latex": 18, "show_circuit_op": 18, "global_phas": 18, "compil": 18, "unitari": 18, "ket_templ": 18, "icol": 18, "col": 18, "\u6700\u521d\u306e\u554f\u984c\u306f": 18, "\u30b2\u30fc\u30c8\u3092cx\u3068": 18, "\u304b\u3089\u4f5c\u308b\u3053\u3068\u3067\u3059": 18, "cp\u30b2\u30fc\u30c8\u3068\u306f": 18, "2\u91cf\u5b50\u30d3\u30c3\u30c8\u30b2\u30fc\u30c8\u3067": 18, "\u4e8c\u3064\u306e\u91cf\u5b50\u30d3\u30c3\u30c8\u304c\u3068\u3082\u306b": 18, "\u3067\u3042\u308b\u3088\u3046\u306a\u8a08\u7b97\u57fa\u5e95\u306e\u4f4d\u76f8\u3092": 18, "\u524d\u9032\u3055\u305b\u308b\u30b2\u30fc\u30c8\u306e\u3053\u3068\u3067\u3057\u305f": 18, "\u307e\u305a\u5236\u5fa1": 18, "cr_z": 18, "\u304b\u3089\u4f5c\u308b\u3053\u3068\u3092\u8003\u3048\u3066\u307f\u307e\u3057\u3087\u3046": 18, "\u306e\u4f5c\u7528\u306f": 18, "\u5de6\u3092\u5236\u5fa1\u30d3\u30c3\u30c8": 18, "\u53f3\u3092\u6a19\u7684\u30d3\u30c3\u30c8\u3068\u3057\u3066": 18, "\u304c\u4f5c\u308c\u308c\u3070": 18, "\u5236\u5fa1\u30d3\u30c3\u30c8\u306b": 18, "\u3092\u304b\u3051\u308b\u3053\u3068\u3067": 18, "\u306e\u4f4d\u76f8\u3092\u63c3\u3048\u308b\u3053\u3068\u304c\u3067\u304d": 18, "\u30b2\u30fc\u30c8\u306b\u5168\u4f53\u4f4d\u76f8\u304c\u304b\u304b\u3063\u305f\u72b6\u614b\u304c\u5b9f\u73fe\u3067\u304d\u307e\u3059": 18, "\u5168\u4f53\u4f4d\u76f8\u306f\u7121\u8996\u3057\u3066\u3044\u3044\u306e\u3067": 18, "\u305d\u308c\u3067": 18, "\u30b2\u30fc\u30c8\u306e\u5b8c\u6210\u3067\u3059": 18, "\u3067\u631f\u3080\u3068": 18, "\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u306e\u7b26\u53f7\u3092\u53cd\u8ee2\u3055\u305b\u305f\u306e\u3068\u540c\u3058\u4f5c\u7528\u304c\u5f97\u3089\u308c\u307e\u3059": 18, "cp_circuit": 18, "\u3092\u5b9f\u88c5\u3059\u308b\u3088\u3046\u306b\u30b2\u30fc\u30c8\u3092\u52a0\u3048\u3066\u304f\u3060\u3055\u3044": 18, "phi\u306f\u666e\u901a\u306e\u5909\u6570\u306e\u3088\u3046\u306b\u4f7f\u3048\u307e\u3059": 18, "\u4f8b": [18, 20], "phi\u306b\u5177\u4f53\u7684\u306a\u5024\u3092\u5165\u308c\u3066": 18, "cp\u304c\u5b9f\u88c5\u3055\u308c\u3066\u3044\u308b\u3053\u3068\u3092\u78ba\u8a8d": 18, "phi_valu": 18, 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\ No newline at end of file
diff --git a/shor.html b/shor.html
index 660884f..8537ada 100644
--- a/shor.html
+++ b/shor.html
@@ -1249,7 +1249,7 @@ <h1>素因数分解アルゴリズムを学習する<a class="headerlink" href="
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/965e9ec4bae62807c13232564c0a7185ae8e244a083aab0cc00c3123c34a4ffa.png" src="_images/965e9ec4bae62807c13232564c0a7185ae8e244a083aab0cc00c3123c34a4ffa.png" />
+<img alt="_images/eb2d205a51013fcf05c31e6ac225cf459a0a1f090899823f90ec0b344e747763.png" src="_images/eb2d205a51013fcf05c31e6ac225cf459a0a1f090899823f90ec0b344e747763.png" />
 </div>
 </div>
 </section>
@@ -1279,9 +1279,9 @@ <h1>素因数分解アルゴリズムを学習する<a class="headerlink" href="
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Register Output    Phase
 ------------------------
 128      128/256 = 0.500
-192      192/256 = 0.750
  64       64/256 = 0.250
   0        0/256 = 0.000
+192      192/256 = 0.750
 </pre></div>
 </div>
 </div>
@@ -1307,9 +1307,9 @@ <h1>素因数分解アルゴリズムを学習する<a class="headerlink" href="
 <div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>     Phase   Fraction   Guess for r
 -------------------------------------
      0.500       1/ 2             2
-     0.750       3/ 4             4
      0.250       1/ 4             4
      0.000       0/ 1             1
+     0.750       3/ 4             4
 </pre></div>
 </div>
 </div>
diff --git a/vqc_machine_learning.html b/vqc_machine_learning.html
index 41b17eb..0b0a62b 100644
--- a/vqc_machine_learning.html
+++ b/vqc_machine_learning.html
@@ -848,7 +848,7 @@ <h3><a class="toc-backref" href="#id21">測定とモデル出力<a id='func_meas
 </div>
 </div>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7ff4b82358d0&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f7a3429f310&gt;]
 </pre></div>
 </div>
 <img alt="_images/d68c3ccb0212f1e018bf68cf9abbd1171f550ca3412171077558c62ff4cea4d6.png" src="_images/d68c3ccb0212f1e018bf68cf9abbd1171f550ca3412171077558c62ff4cea4d6.png" />
@@ -870,7 +870,7 @@ <h3><a class="toc-backref" href="#id21">測定とモデル出力<a id='func_meas
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/93ac1f3c050ec9359629eec944a2957937676d109e1eaa58efa3dcfd3a4e049c.png" src="_images/93ac1f3c050ec9359629eec944a2957937676d109e1eaa58efa3dcfd3a4e049c.png" />
+<img alt="_images/6f39c8a6ab3407e0c2f1265152157dcebde01c976f0118c5ee046cdb8d2bb252.png" src="_images/6f39c8a6ab3407e0c2f1265152157dcebde01c976f0118c5ee046cdb8d2bb252.png" />
 </div>
 </div>
 <p>生成された図を確認してください。ノイズを印加した学習データの分布から、元の関数<span class="math notranslate nohighlight">\(f(x)=x^3\)</span>をおおよそ導き出せていることが分かると思います。</p>
diff --git a/vqe.html b/vqe.html
index 43b4e27..b7fa80f 100644
--- a/vqe.html
+++ b/vqe.html
@@ -655,9 +655,9 @@ <h3><a class="toc-backref" href="#id27">変分フォームを使った実例</a>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.75
-&lt;Y&gt; = 0.43301270189221924
-&lt;Z&gt; = 0.5000000000000002
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.7499999999999998
+&lt;Y&gt; = 0.43301270189221913
+&lt;Z&gt; = 0.5
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@@ -744,9 +744,9 @@ <h3><a class="toc-backref" href="#id27">変分フォームを使った実例</a>
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-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.7592
-&lt;Y&gt; = 0.4408
-&lt;Z&gt; = 0.4996
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.7582
+&lt;Y&gt; = 0.4432
+&lt;Z&gt; = 0.4852
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@@ -844,7 +844,7 @@ <h3><a class="toc-backref" href="#id27">変分フォームを使った実例</a>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9998472042379072
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9998472042379076
 </pre></div>
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@@ -871,7 +871,7 @@ <h4><a class="toc-backref" href="#id28">Estimatorの利用</a><a class="headerli
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.7398, 0.432 , 0.497 ])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.7458, 0.42  , 0.5012])
 </pre></div>
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@@ -938,7 +938,7 @@ <h4><a class="toc-backref" href="#id28">Estimatorの利用</a><a class="headerli
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9990917238925524
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.9990917238925522
 </pre></div>
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diff --git a/vqe_tracking.html b/vqe_tracking.html
index de2b2d5..4874903 100644
--- a/vqe_tracking.html
+++ b/vqe_tracking.html
@@ -602,7 +602,7 @@ <h4><a class="toc-backref" href="#id19">QUBO</a><a class="headerlink" href="#qub
 \]</div>
 <p>を最小化する<span class="math notranslate nohighlight">\(\{T_i\}\)</span>を求めます。ここで<span class="math notranslate nohighlight">\(a_i\)</span>は上で決めたセグメント<span class="math notranslate nohighlight">\(i\)</span>のスコア、<span class="math notranslate nohighlight">\(b_{ij}\)</span>はセグメント<span class="math notranslate nohighlight">\(i\)</span>と<span class="math notranslate nohighlight">\(j\)</span>のペアのスコアです。<span class="math notranslate nohighlight">\(a_i\)</span>の値が小さい（検出器中心を向いている）、かつ<span class="math notranslate nohighlight">\(b_{ij}\)</span>の値が小さい（正しい飛跡と無矛盾な）ペアを組んでいるセグメントを採用し、そうでないものを棄却するほど、<span class="math notranslate nohighlight">\(O\)</span>の値は小さくなります。採用すべきセグメントが決まれば、それに基づいてすべての粒子飛跡を再構成できるので、この最小化問題を解くことがトラッキングに対応します。</p>
 <p>上のような形式の最適化問題を<strong>QUBO</strong>（<em>Quadratic Unconstrained Binary Optimization</em>、2次制約無し2値最適化）と呼びます。一見特殊な形式ですが、実は様々な最適化問題（例えば有名な巡回セールスマン問題なども）がQUBOの形に落とし込めることが知られています。また、ここでは直接関係しませんが、量子アニーリングマシンと呼ばれるタイプの量子コンピュータでは、QUBOを解くことが動作の基本です。</p>
-<p>それでは、まずスコア<span class="math notranslate nohighlight">\(a_{i}\)</span>と<span class="math notranslate nohighlight">\(b_{ij}\)</span>を読み出しましょう。この後のプログラミングを簡単にするため、二つのスコアセットは一つの二次元配列<code class="docutils literal notranslate"><span class="pre">b</span></code>に記録されており、<code class="docutils literal notranslate"><span class="pre">b[i,</span> <span class="pre">i]</span></code>が<span class="math notranslate nohighlight">\(a_i\)</span>に対応するようになっています。</p>
+<p>それでは、まずスコア<span class="math notranslate nohighlight">\(a_{i}\)</span>と<span class="math notranslate nohighlight">\(b_{ij}\)</span>を読み出しましょう。</p>
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 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># スコアの読み込み</span>
@@ -642,7 +642,7 @@ <h4><a class="toc-backref" href="#id20">Ising形式</a><a class="headerlink" hre
 \[
 H(h, J, s) = \sum_{i=1}^N h_i Z_i + \sum_{i=1}^N \sum_{j&lt;i}^N J_{ij} Z_i Z_j + \text{(constant)}
 \]</div>
-<p>が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の<span class="math notranslate nohighlight">\(constant\)</span>はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。</p>
+<p>が得られます。これは物理を始め自然科学の様々な場面で登場するIsing模型のハミルトニアンと同じ形になっています。右辺の<span class="math notranslate nohighlight">\(\text{constant}\)</span>はハミルトニアンの定数項で、変分法において意味を持たないので以降は無視します。</p>
 <p>以下のセルで、上の処方に従ってIsingハミルトニアンの係数<span class="math notranslate nohighlight">\(h_i\)</span>と<span class="math notranslate nohighlight">\(J_{ij}\)</span>を計算してください。</p>
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