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diff --git a/_sources/addition_on_ibmq.ipynb b/_sources/addition_on_ibmq.ipynb
index 7076aae..8d1bba8 100644
--- a/_sources/addition_on_ibmq.ipynb
+++ b/_sources/addition_on_ibmq.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "46ca1530",
+   "id": "37c5ef79",
    "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": "945a26ec",
+   "id": "eb0f7157",
    "metadata": {
     "tags": [
      "remove-output"
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3455a1c9",
+   "id": "67f1391b",
    "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": "1612b1d9",
+   "id": "c39c8798",
    "metadata": {
     "tags": [
      "remove-output",
@@ -107,7 +107,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6e16d130",
+   "id": "e952cfda",
    "metadata": {
     "tags": [
      "remove-output"
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "01aab7ec",
+   "id": "dbc59c78",
    "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": "02c0c89c",
+   "id": "084719d5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6c3254c6",
+   "id": "6ea88a5e",
    "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": "1a12bc8d",
+   "id": "621b549f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -268,7 +268,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "867156e6",
+   "id": "63465337",
    "metadata": {
     "tags": [
      "remove-input"
@@ -287,7 +287,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "e76b222a",
+   "id": "d064832e",
    "metadata": {
     "tags": [
      "remove-output",
@@ -339,7 +339,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "6f06e2e8",
+   "id": "7f468607",
    "metadata": {
     "tags": [
      "remove-input"
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "79f222d7",
+   "id": "2fd163dc",
    "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": "aab21eca",
+   "id": "4c5088d4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bcbc3c74",
+   "id": "2688f994",
    "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": "f027260b",
+   "id": "16c3c4db",
    "metadata": {
     "tags": [
      "remove-input"
@@ -473,7 +473,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1c21be9f",
+   "id": "40a95288",
    "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": "15210dd3",
+   "id": "632a572a",
    "metadata": {},
    "source": [
     "## Quantum Volume\n",
@@ -502,7 +502,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "e65b800e",
+   "id": "9712a223",
    "metadata": {
     "tags": [
      "remove-output",
@@ -533,7 +533,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "2879dd6c",
+   "id": "ae222ec4",
    "metadata": {
     "tags": [
      "remove-output",
@@ -579,7 +579,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "571d1bcb",
+   "id": "ad41c7c8",
    "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": "de6669d3",
+   "id": "9a83e11e",
    "metadata": {
     "tags": [
      "remove-output",
@@ -623,7 +623,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7eb61bcf",
+   "id": "04bb3381",
    "metadata": {
     "tags": [
      "remove-output",
@@ -657,7 +657,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "4c23b111",
+   "id": "999ce3d5",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -693,7 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "95c375f2",
+   "id": "af1784c5",
    "metadata": {
     "tags": [
      "remove-output",
diff --git a/_sources/chsh_inequality.ipynb b/_sources/chsh_inequality.ipynb
index e226ee6..cedd366 100644
--- a/_sources/chsh_inequality.ipynb
+++ b/_sources/chsh_inequality.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e61cdc34",
+   "id": "0377070c",
    "metadata": {},
    "source": [
     "# CHSH不等式の破れを確認する"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aaf49f9",
+   "id": "9e1b9c08",
    "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": "6aac94ee",
+   "id": "930edce8",
    "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": "d73c58e3",
+   "id": "cc502000",
    "metadata": {},
    "source": [
     "## CHSH不等式\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd447908",
+   "id": "f5be3826",
    "metadata": {},
    "source": [
     "## Qiskitの基本構造\n",
@@ -74,7 +74,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0ad5f6c",
+   "id": "d692efdd",
    "metadata": {},
    "source": [
     "### 量子ビット、量子レジスタ\n",
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a79e111a",
+   "id": "316579ad",
    "metadata": {},
    "source": [
     "### ゲート、回路、測定\n",
@@ -190,7 +190,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a5d6a955",
+   "id": "7ab5a554",
    "metadata": {},
    "source": [
     "### 量子計算結果の解析\n",
@@ -204,7 +204,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4afc370e",
+   "id": "26f8fb5d",
    "metadata": {},
    "source": [
     "(common_gates)=\n",
@@ -264,7 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "edde433b",
+   "id": "32d403c2",
    "metadata": {
     "tags": [
      "remove-output"
@@ -298,7 +298,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "ad01f5b1",
+   "id": "a2bbf69c",
    "metadata": {},
    "outputs": [
     {
@@ -322,7 +322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d86b0ee0",
+   "id": "f49bbbd5",
    "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": "07975632",
+   "id": "788cfd31",
    "metadata": {},
    "outputs": [
     {
@@ -413,7 +413,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b88e9ab1",
+   "id": "d2b00a5c",
    "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": "f12e2776",
+   "id": "84250d90",
    "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": "b3481f96",
+   "id": "a22d9006",
    "metadata": {},
    "outputs": [
     {
@@ -477,7 +477,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "db2e6a3d",
+   "id": "748ea798",
    "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": "cec3d3fe",
+   "id": "a886c23e",
    "metadata": {},
    "outputs": [
     {
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbb01991",
+   "id": "d567ea7b",
    "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": "4780b811",
+   "id": "68472bbb",
    "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": "fcd6ea9f",
+   "id": "0e0a7d33",
    "metadata": {},
    "outputs": [
     {
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d9bc036",
+   "id": "fcba9237",
    "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": "2e2f1573",
+   "id": "e4aeebc5",
    "metadata": {},
    "source": [
     "## 回路を実機で実行する\n",
@@ -716,7 +716,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "22ac8c0f",
+   "id": "83eaaf10",
    "metadata": {
     "tags": [
      "remove-output",
@@ -769,7 +769,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1755da0b",
+   "id": "a29a8a66",
    "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": "b74e3348",
+   "id": "effbec7e",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -811,7 +811,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f703a121",
+   "id": "994be1bd",
    "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": "01f75a80",
+   "id": "52f32ee9",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -856,7 +856,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a1b275e",
+   "id": "b5756832",
    "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": "31449b73",
+   "id": "24eede9c",
    "metadata": {},
    "source": [
     "## 量子測定結果の解析\n",
@@ -883,7 +883,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b9fba7f4",
+   "id": "893cd28b",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -922,7 +922,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "030b73bb",
+   "id": "ba500b39",
    "metadata": {
     "tags": [
      "remove-cell"
@@ -946,7 +946,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dce71ba0",
+   "id": "6b57fd48",
    "metadata": {},
    "source": [
     "````{tip}\n",
@@ -966,7 +966,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "4ac12029",
+   "id": "1608619c",
    "metadata": {},
    "outputs": [
     {
@@ -990,7 +990,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a87cc273",
+   "id": "d1977e05",
    "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": "a3acc668",
+   "id": "756b235a",
    "metadata": {},
    "outputs": [
     {
@@ -1045,7 +1045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d0998d89",
+   "id": "e3cfc40d",
    "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 649e3c7..be37e75 100644
--- a/_sources/circuit_from_scratch.ipynb
+++ b/_sources/circuit_from_scratch.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "91cd455e",
+   "id": "53955341",
    "metadata": {},
    "source": [
     "# 単純な量子回路をゼロから書く"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e6beb39",
+   "id": "b4293908",
    "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": "981f1c34",
+   "id": "624a687b",
    "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": "3424cc88",
+   "id": "2a67f780",
    "metadata": {
     "tags": [
      "remove-output"
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b1b78a16",
+   "id": "400c700d",
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9633c2ed",
+   "id": "53965c8d",
    "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": "7ee0725e",
+   "id": "60350757",
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fdd10617",
+   "id": "0793d32e",
    "metadata": {},
    "source": [
     "```{caution}\n",
@@ -135,7 +135,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "79fd0564",
+   "id": "f56474a9",
    "metadata": {},
    "outputs": [
     {
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c99788ce",
+   "id": "4fd22efd",
    "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": "8bfae3f5",
+   "id": "086aa0d3",
    "metadata": {},
    "outputs": [
     {
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5729b5e6",
+   "id": "791e2ae4",
    "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": "df940630",
+   "id": "4b5c1d00",
    "metadata": {},
    "source": [
     "(other_gates)=\n",
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "024df49e",
+   "id": "d3f2e1ee",
    "metadata": {},
    "source": [
     "### 1、2量子ビット\n",
@@ -276,7 +276,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "71077af3",
+   "id": "82443d39",
    "metadata": {},
    "source": [
     "### 3量子ビット以上\n",
@@ -304,7 +304,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ffe665d0",
+   "id": "d2e12bce",
    "metadata": {},
    "source": [
     "## 量子状態生成\n",
@@ -318,7 +318,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "529f1984",
+   "id": "b0914534",
    "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": "8524cce1",
+   "id": "2ae377bc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -375,7 +375,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a647a35c",
+   "id": "55420647",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -402,7 +402,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf5922a9",
+   "id": "501c3772",
    "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": "375b2d8d",
+   "id": "8da4c0f0",
    "metadata": {
     "tags": [
      "remove-output"
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f906509f",
+   "id": "50f1e53e",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49166d94",
+   "id": "078d2cf8",
    "metadata": {},
    "source": [
     "### 問題3: GHZ状態\n",
@@ -497,7 +497,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "f4bb41fc",
+   "id": "8405d5ca",
    "metadata": {
     "tags": [
      "remove-output"
@@ -536,7 +536,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "610f6766",
+   "id": "d924011a",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -558,7 +558,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1606ebbc",
+   "id": "ecdfb47b",
    "metadata": {},
    "source": [
     "### 問題4: Equal superposition\n",
@@ -577,7 +577,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "06a78603",
+   "id": "a9d050e4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -618,7 +618,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb138be8",
+   "id": "62283894",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "416f4ed2",
+   "id": "25ae10ae",
    "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": "ca5eebfc",
+   "id": "76a5b503",
    "metadata": {
     "tags": [
      "remove-output"
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2cfb69b",
+   "id": "9282f4ce",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dd2d189c",
+   "id": "7e514d7b",
    "metadata": {},
    "source": [
     "(equal_superposition_with_phase)=\n",
@@ -754,7 +754,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "182fee2f",
+   "id": "02588791",
    "metadata": {
     "tags": [
      "output_scroll",
@@ -803,7 +803,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6707e03e",
+   "id": "dddf7f6b",
    "metadata": {
     "tags": [
      "hide-cell"
@@ -851,7 +851,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fd3e9ce",
+   "id": "25139368",
    "metadata": {},
    "source": [
     "## 量子計算プリミティブ\n",
@@ -861,7 +861,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "318c6f32",
+   "id": "3332f18c",
    "metadata": {},
    "source": [
     "### 条件分岐\n",
@@ -878,7 +878,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "cfedffc5",
+   "id": "3101c9e8",
    "metadata": {},
    "outputs": [
     {
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d806efef",
+   "id": "c6e8888b",
    "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": "fd31117d",
+   "id": "1d03ee21",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle | \\text{final} \\rangle =  - 0.556 |0:11\\rangle + 0.831 |1:11\\rangle$"
+       "$\\displaystyle | \\text{final} \\rangle =  - 0.383 |0:10\\rangle + 0.924 |1:10\\rangle$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -969,7 +969,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34bd5699",
+   "id": "ce137f67",
    "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": "a68a5b76",
+   "id": "486dc0a8",
    "metadata": {},
    "outputs": [
     {
@@ -1015,7 +1015,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca2e61f3",
+   "id": "dd2791ba",
    "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": "51178488",
+   "id": "14ba74f2",
    "metadata": {},
    "outputs": [
     {
@@ -1048,7 +1048,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c09a4154",
+   "id": "4aae234b",
    "metadata": {},
    "source": [
     "### 関数\n",
@@ -1071,7 +1071,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "ef0c82ee",
+   "id": "bfac6e59",
    "metadata": {},
    "outputs": [
     {
@@ -1111,7 +1111,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "974f1aca",
+   "id": "8fbf2c49",
    "metadata": {},
    "outputs": [
     {
@@ -1134,7 +1134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f880d87",
+   "id": "74003f66",
    "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": "ff04cb83",
+   "id": "ef7cf767",
    "metadata": {},
    "source": [
     "#### SWAPテスト\n",
@@ -1217,13 +1217,13 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "d015402c",
+   "id": "b6dabc7f",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f1e3071b2e0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f97d80f0670>"
       ]
      },
      "execution_count": 18,
@@ -1295,7 +1295,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "5896dc5e",
+   "id": "b2979221",
    "metadata": {},
    "outputs": [
     {
@@ -1316,7 +1316,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "34a75547",
+   "id": "f4526298",
    "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": "765dcc2d",
+   "id": "871c272b",
    "metadata": {},
    "outputs": [
     {
@@ -1392,7 +1392,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "b6794bd5",
+   "id": "7a0be2c6",
    "metadata": {},
    "outputs": [
     {
@@ -1415,7 +1415,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "508a6231",
+   "id": "ffbb7e12",
    "metadata": {},
    "source": [
     "で、$|\\braket{\\psi}{\\phi}|^2$は"
@@ -1424,7 +1424,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "8a7aef4d",
+   "id": "8e965b22",
    "metadata": {},
    "outputs": [
     {
@@ -1443,7 +1443,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "75086629",
+   "id": "ef0e6fa9",
    "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": "2a3a7774",
+   "id": "4461fe4f",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.203244\n"
+      "0.203578\n"
      ]
     }
    ],
@@ -1475,7 +1475,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c55a4af6",
+   "id": "10858d6c",
    "metadata": {},
    "source": [
     "です。"
@@ -1483,7 +1483,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2261ada3",
+   "id": "cfb942b3",
    "metadata": {},
    "source": [
     "(inverse_circuit)=\n",
@@ -1504,7 +1504,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "0cb698ea",
+   "id": "a828008b",
    "metadata": {},
    "outputs": [
     {
@@ -1534,7 +1534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ab1ccef",
+   "id": "23c1c8a3",
    "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": "f6f6d95f",
+   "id": "32714285",
    "metadata": {},
    "source": [
     "```{toggle}\n",
@@ -1609,7 +1609,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "667f4f32",
+   "id": "1fd49788",
    "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": "f5f441e0",
+   "id": "b75bb836",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.202827\n"
+      "0.202314\n"
      ]
     }
    ],
@@ -1640,7 +1640,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7bcf37f4",
+   "id": "bd358668",
    "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": "ec544e7a",
+   "id": "f73e2c48",
    "metadata": {},
    "source": [
     "```{toggle}\n",
@@ -1686,7 +1686,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b21a3d90",
+   "id": "ee8a3926",
    "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": "d3f81725",
+   "id": "31d96565",
    "metadata": {},
    "outputs": [
     {
@@ -1753,7 +1753,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2949e4c",
+   "id": "c0683530",
    "metadata": {},
    "source": [
     "入力ビットの状態は"
@@ -1762,7 +1762,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "26ae5592",
+   "id": "d2252051",
    "metadata": {},
    "outputs": [
     {
@@ -1785,7 +1785,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5484902e",
+   "id": "79b07d52",
    "metadata": {},
    "source": [
     "回路の終状態は"
@@ -1794,7 +1794,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "3117adae",
+   "id": "879dcde1",
    "metadata": {},
    "outputs": [
     {
@@ -1817,7 +1817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9b0b3ce",
+   "id": "8177515a",
    "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 0683176..35ef4c2 100644
--- a/_sources/dynamics_simulation.ipynb
+++ b/_sources/dynamics_simulation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "9528425c",
+   "id": "8e430066",
    "metadata": {},
    "source": [
     "# 物理系を表現する\n",
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8215a121",
+   "id": "0105a048",
    "metadata": {},
    "source": [
     "## 量子系のダイナミクスとは\n",
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8bc937de",
+   "id": "be09ab9e",
    "metadata": {},
    "source": [
     "### 例：核磁気の歳差運動\n",
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "56dbacf1",
+   "id": "589f88cc",
    "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": "5e3bf11b",
+   "id": "14c12f74",
    "metadata": {
     "tags": [
      "remove-input"
@@ -152,7 +152,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "67ff9f4a",
+   "id": "285c8635",
    "metadata": {
     "tags": [
      "remove-input"
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8edb8b25",
+   "id": "a63f9f55",
    "metadata": {},
    "source": [
     "### ハミルトニアンの対角化\n",
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8cadd305",
+   "id": "76ce044e",
    "metadata": {},
    "source": [
     "### 鈴木・トロッター分解\n",
@@ -314,7 +314,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d881942",
+   "id": "6d08c9a9",
    "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": "5b31afab",
+   "id": "e25f40ef",
    "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": "081956f6",
+   "id": "51aaf431",
    "metadata": {
     "tags": [
      "remove-input"
@@ -439,7 +439,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5761863",
+   "id": "f1ee0e6f",
    "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": "87411d42",
+   "id": "8d067368",
    "metadata": {
     "tags": [
      "remove-input"
@@ -505,7 +505,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7c9467f9",
+   "id": "f049bb36",
    "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": "a1c24ffb",
+   "id": "87e1a833",
    "metadata": {
     "tags": [
      "remove-input"
@@ -551,7 +551,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "169c3442",
+   "id": "281e7fe1",
    "metadata": {},
    "source": [
     "です[^sgate]。\n",
@@ -566,7 +566,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3855916f",
+   "id": "ec07f8e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +585,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4b7ca1c3",
+   "id": "96355522",
    "metadata": {},
    "outputs": [
     {
@@ -648,7 +648,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b0488b33",
+   "id": "21929632",
    "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": "22f596ea",
+   "id": "a2570a43",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc3012bf",
+   "id": "2a269844",
    "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": "0b7a9890",
+   "id": "6c6cd011",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -747,7 +747,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "fcf588a5",
+   "id": "2cf37d78",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -780,7 +780,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "e12e3232",
+   "id": "bfd6de05",
    "metadata": {
     "tags": [
      "raises-exception",
diff --git a/_sources/extreme_simd.ipynb b/_sources/extreme_simd.ipynb
index ada98c4..401c4d9 100644
--- a/_sources/extreme_simd.ipynb
+++ b/_sources/extreme_simd.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "bb843166",
+   "id": "3bf32e7a",
    "metadata": {},
    "source": [
     "# 計算をする量子回路の実装"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38223a6c",
+   "id": "8d3cbff5",
    "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": "d9295c63",
+   "id": "d124298e",
    "metadata": {},
    "source": [
     "## 量子計算の特徴\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "396a4a3a",
+   "id": "e89cf273",
    "metadata": {},
    "source": [
     "(QFT)=\n",
@@ -86,7 +86,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0b576f34",
+   "id": "2903b8ca",
    "metadata": {
     "tags": [
      "remove-output"
@@ -121,7 +121,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3ff34f74",
+   "id": "162d3631",
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6ad99644",
+   "id": "c191920d",
    "metadata": {},
    "outputs": [
     {
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7cf075cf",
+   "id": "4a4b892e",
    "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": "fedef709",
+   "id": "3f5818a4",
    "metadata": {},
    "source": [
     "(fourier_addition)=\n",
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "181375bd",
+   "id": "7b47759b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7d098fb2",
+   "id": "9e676def",
    "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": "bd61b23f",
+   "id": "bfe71822",
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9d14af75",
+   "id": "fbd97f65",
    "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": "79b9021e",
+   "id": "670eaf4a",
    "metadata": {},
    "outputs": [
     {
@@ -562,7 +562,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b44cfef4",
+   "id": "920d46e7",
    "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": "8d7fa861",
+   "id": "3913af39",
    "metadata": {},
    "source": [
     "## 足し算の並列化\n",
@@ -593,7 +593,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "3e1be0b3",
+   "id": "497135eb",
    "metadata": {},
    "outputs": [
     {
@@ -633,7 +633,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a4fbd814",
+   "id": "48f90776",
    "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": "0bbcf3e1",
+   "id": "fd04409a",
    "metadata": {
     "tags": [
      "remove-output"
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "326093cc",
+   "id": "a8a0fa77",
    "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": "bedeefdd",
+   "id": "f8aa5fd1",
    "metadata": {},
    "outputs": [
     {
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "122a52d6",
+   "id": "cee5374f",
    "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": "1c43e51c",
+   "id": "ae783eec",
    "metadata": {},
    "source": [
     "(transpilation)=\n",
@@ -755,7 +755,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb118a58",
+   "id": "d49e26cc",
    "metadata": {},
    "source": [
     "### 回路の比較\n",
@@ -770,7 +770,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "bb257e7b",
+   "id": "b9daab7e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -817,7 +817,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1035ba1f",
+   "id": "89099ccf",
    "metadata": {
     "tags": [
      "remove-output"
@@ -842,8 +842,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Number of operations in the original circuit: 482\n",
-      "  Breakdown: N(Rz)=199, N(X)=14, N(SX)=78, N(CNOT)=178\n",
+      "Number of operations in the original circuit: 519\n",
+      "  Breakdown: N(Rz)=226, N(X)=13, N(SX)=104, N(CNOT)=163\n",
       "Number of operations in the optimized circuit: 383\n"
      ]
     },
@@ -884,7 +884,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3457cef6",
+   "id": "388dd22d",
    "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",
@@ -897,7 +897,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "8485debe",
+   "id": "02ad6eed",
    "metadata": {
     "tags": [
      "remove-output"
@@ -953,7 +953,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c880c358",
+   "id": "58a2f0ae",
    "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 a3b4dc8..d4bbd45 100644
--- a/_sources/grover.ipynb
+++ b/_sources/grover.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2dfad907",
+   "id": "5d9bd8e5",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe2b1678",
+   "id": "569446cc",
    "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": "02b542d8",
+   "id": "402c524b",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62a35a52",
+   "id": "d1947e85",
    "metadata": {},
    "source": [
     "(database)=\n",
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c384a9c2",
+   "id": "c626607d",
    "metadata": {},
    "source": [
     "(grover)=\n",
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9f7e1336",
+   "id": "5cbb3666",
    "metadata": {},
    "source": [
     "(grover_phaseoracle)=\n",
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5032aba",
+   "id": "dadbd954",
    "metadata": {},
    "source": [
     "(grover_circuit)=\n",
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "056200a0",
+   "id": "b02006ee",
    "metadata": {},
    "source": [
     "(grover_superposition)=\n",
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c42c8e3",
+   "id": "44dd3ac4",
    "metadata": {},
    "source": [
     "(grover_geometry)=\n",
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9598425f",
+   "id": "22b9dc19",
    "metadata": {},
    "source": [
     "(grover_oracle)=\n",
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0990d6fa",
+   "id": "b3b09eb3",
    "metadata": {},
    "source": [
     "(grover_diffuser)=\n",
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ee12ac22",
+   "id": "24c7f602",
    "metadata": {},
    "source": [
     "(grover_amp)=\n",
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8de0955c",
+   "id": "0f14e77a",
    "metadata": {},
    "source": [
     "(grover_multidata)=\n",
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68f89654",
+   "id": "663cf154",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b9a8d96d",
+   "id": "2913e9cc",
    "metadata": {},
    "source": [
     "(imp_qiskit)=\n",
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "1446195f",
+   "id": "1646af18",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -389,7 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "54052a4d",
+   "id": "a307f747",
    "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": "748474bb",
+   "id": "28e0e4ec",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -443,7 +443,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7ff33d556b00>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7f0758586a70>"
       ]
      },
      "execution_count": 2,
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8360e122",
+   "id": "640a6b47",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -516,7 +516,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "f37f9790",
+   "id": "1bad14ea",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -568,7 +568,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1a7d9280",
+   "id": "db0e2a0a",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -619,7 +619,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "f0ca2cd4",
+   "id": "a43cf253",
    "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>"
       ]
@@ -673,7 +673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "feea19ca",
+   "id": "51d8016d",
    "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": "466fe54b",
+   "id": "3203f7a0",
    "metadata": {},
    "source": [
     "(imp_qc)=\n",
@@ -695,7 +695,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "98952776",
+   "id": "47668221",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -745,7 +745,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "5e5baf82",
+   "id": "43a79ca4",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -782,7 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "8898172a",
+   "id": "62c47518",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -817,7 +817,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "334ac07b",
+   "id": "83fb681a",
    "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": "065cd658",
+   "id": "7a000672",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -843,7 +843,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ca911c59",
+   "id": "0257e41c",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -878,7 +878,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "34b029d3",
+   "id": "2aa23346",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -887,7 +887,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -905,7 +905,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "03b20f00",
+   "id": "f965a78d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -920,7 +920,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f4540a30",
+   "id": "0c96a0e4",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -929,7 +929,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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6SmPHjtV1110nSZowYYJeeeUVZWRk6Oabb75AKQEAgD/z6ZGZ8+nXr58+/vhj7du3T4ZhaN26ddq5c6eGDh1a52sqKipUUlJS7QEAABouvy4zL7zwghISEtS2bVs1adJEw4YN00svvaSBAwfW+ZrZs2crMjLS+Tg9Hw4AAGiY/L7MfP311/r444+1ZcsWzZs3T8nJyVqzZk2dr5k1a5aKi4udj9Pz4QAAgIbJo2Nmtm3bpl69enlk4rzjx4/rkUce0bJly3TTTTdJknr06KHs7Gw9++yzdd6hOyQkRCEhIfX++gAAwBo8fmTGU1d6V1ZWqrKyUgEB1SMGBgbK4XB45GsAAADrc+vIzKhRo865vri4WDabzeX9lZaWateuXc7ndrtd2dnZioqKUrt27XTttddqxowZCgsLU/v27ZWenq5//OMf+utf/+pObAAA0IC5NWlecHCwhgwZotatW9e6vrCwUCtWrHD5NNP69es1aNCgGsvHjh2rxYsXq6CgQLNmzdJnn32mwsJCtW/fXhMmTNC0adNcLk1MmgcAgPW48/ntVpnp0aOHpkyZovHjx9e6Pjs7W1deeaVf3WySMgMAgPV4bQbgK6+8UllZWXWuDwkJUbt27dzZJQAAQL24dWSmoqJCVVVVuuiii7yZyaM4MgMAgPW48/nt1gBgLnkGAAD+xuXTTGVlZW7t2N3tAQAAzHC5zHTq1Elz5sxRfn5+ndsYhqHVq1dr+PDhWrBggUcCAgAAnIvLp5nWr1+vRx55RE888YR69uyp3r17KzY2VqGhoTpy5IhycnK0adMmBQUFadasWfrv//5vb+YGAACQ5OYAYEnau3ev3nvvPX3xxRfas2ePjh8/rosvvliJiYlKSkrS8OHDFRgY6K28bmMAMAAA1uO1eWasiDIDAID1eG2eGQAAAH9DmQEAAJZGmQEAAJZGmQEAAJZGmQEAAJZmqsysXLlSX375pfP5Sy+9pCuuuEK///3vdeTIEY+FAwAAOB9TZWbGjBkqKSmRJH333XeaPn26brzxRtntdqWkpHg0oL+qchjatPuwPsrep027D6vK0aCvcAcAwG+5daPJ0+x2uxISEiRJ77//vn7961/r6aefVlZWlm688UaPBvRHK7fnK215jvKLy53LYiJDlToiQcO6xfgwGQAAjY+pIzNNmjTRsWPHJElr1qzR0KFDJUlRUVHOIzYN1crt+Zq4JKtakZGkguJyTVySpZXb6753FQAA8DxTR2YGDBiglJQU9e/fXxkZGXr33XclSTt37lTbtm09GtCfVDkMpS3PUW0nlAxJNklpy3M0JKGNAgNsFzgdAACNk6kjMy+++KKCgoL073//WwsXLtQll1wiSfr00081bNgwjwb0Jxn2whpHZM5kSMovLleGvfDChQIAoJEzdWSmXbt2WrFiRY3lzz33XL0D+bODR+suMma2AwAA9WeqzEiSw+HQrl27dPDgQTkcjmrrBg4cWO9g/ig6PNSj2wEAgPozVWa+/vpr/f73v9eePXt09k23bTabqqqqPBLO3/SJj1JMZKgKistrHTdjk9QmMlR94qMudDQAABotU2Nm7r//fvXu3Vvbt29XYWGhjhw54nwUFjbc8SKBATaljjh1SfrZw3tPP08dkcDgXwAALiCbcfahFRc0bdpU27ZtU6dOnbyRyaNKSkoUGRmp4uJiRUREeGSfzDMDAIB3ufP5beo009VXX61du3ZZosx4w7BuMRqS0EYZ9kIdPFqu6PBTp5Y4IgMAwIVnqsw8+OCDmj59ugoKCtS9e3cFBwdXW9+jRw+PhPNngQE29e3Y0tcxAABo9EydZgoIqDnUxmazyTAMvxsA7I3TTAAAwLu8fprJbrebCgYAAOBppspM+/btPZ0DAADAFNOT5u3evVvz589Xbm6uJCkhIUFTpkxRx44dPRYOAADgfEzNM7Nq1SolJCQoIyNDPXr0UI8ePbR582Z17dpVq1ev9nRGAACAOpkaAJyYmKikpCTNmTOn2vKZM2fqs88+U1ZWlscC1hcDgAEAsB53Pr9NHZnJzc3V+PHjaywfN26ccnJyzOwSAADAFFNlplWrVsrOzq6xPDs7W9HR0fXNBAAA4DJTA4Dvu+8+TZgwQT/99JP69esnSdq4caPmzp2rlJQUjwYEAAA4F1NjZgzD0Pz58zVv3jzt379fkhQbG6sZM2Zo8uTJstn8Z1p/xswAAGA97nx+myozZzp69KgkKTw8vD678RrKDAAA1uP1GYDP5K8lBgAANA4ul5levXpp7dq1atGihRITE895KsmfLs0GAAANm8tl5pZbblFISIjzz/40LgYAADRe9R4zUx8bNmzQX/7yF23ZskX5+flatmyZRo4cWW2b3NxcPfzww0pPT9fJkyeVkJCg999/X+3atXPpazBm5tyqHIYy7IU6eLRc0eGh6hMfpcAAiioAwLe8PmbmV7/6lTIzM9WyZctqy4uKitSrVy/99NNPLu2nrKxMPXv21Lhx4zRq1Kga63fv3q0BAwZo/PjxSktLU0REhL7//nuFhoaaiY2zrNyer7TlOcovLncui4kMVeqIBA3rFuPDZAAAuM7UkZmAgAAVFBTUmCDvwIEDiouL04kTJ9wPYrPVODIzZswYBQcH66233nJ7f6dxZKZ2K7fna+KSLJ395p8+JrPwzl4UGgCAz3jtyMzHH3/s/POqVasUGRnpfF5VVaW1a9cqPj7ezbi1czgc+uSTT/TQQw8pKSlJW7duVXx8vGbNmlXjVBTcU+UwlLY8p0aRkSRDpwpN2vIcDUlowyknAIDfc6vMnC4RNptNY8eOrbYuODhYHTp00Lx58zwS7ODBgyotLdWcOXP05JNPau7cuVq5cqVGjRqldevW6dprr631dRUVFaqoqHA+Lykp8UiehiTDXljt1NLZDEn5xeXKsBeqb8eWdW4HAIA/cKvMOBwOSVJ8fLwyMzN18cUXeyXUmV/rlltu0bRp0yRJV1xxhb766istWrSozjIze/ZspaWleS1XQ3DwaN1Fxsx2AAD4kqkbTdrtdq8WGUm6+OKLFRQUpISEhGrLu3Tpor1799b5ulmzZqm4uNj5yMvL82pOK4oOd20AtavbAQDgS6ZnAC4rK1N6err27t1bY8Dv5MmT6x2sSZMmuuqqq7Rjx45qy3fu3Kn27dvX+bqQkBDnfDioXZ/4KMVEhqqguLzWcTM2SW0iT12mDQCAvzNVZrZu3aobb7xRx44dU1lZmaKiovTLL7/ooosuUnR0tMtlprS0VLt27XI+t9vtys7OVlRUlNq1a6cZM2botttu08CBAzVo0CCtXLlSy5cv1/r1683Exv8KDLApdUSCJi7Jkk2qVmhOD/dNHZHA4F8AgCWYujT7uuuuU+fOnbVo0SJFRkZq27ZtCg4O1p133qkpU6bUOmdMbdavX69BgwbVWD527FgtXrxYkvT6669r9uzZ+vnnn3XZZZcpLS1Nt9xyi8tZuTS7bswzAwDwV16/a3bz5s21efNmXXbZZWrevLk2bdqkLl26aPPmzRo7dqx++OEH0+E9jTJzbswADADwR16fATg4OFgBAafGDkdHR2vv3r3q0qWLIiMjGXBrMYEBNi6/BgBYmqkyk5iYqMzMTF166aW69tpr9fjjj+uXX37RW2+9pW7dunk6IwAAQJ1MXZr99NNPKybm1JiKp556Si1atNDEiRN16NAhvfrqqx4NCAAAcC4+vWv2hcCYGQAArMedz29TR2aefPJJ2e12U+EAAAA8yVSZee+999SpUyf169dPL7/8sn755RdP5wIAAHCJqTKzbds2ffvtt7ruuuv07LPPKjY2VjfddJOWLl2qY8eOeTojAABAnTwyZmbjxo1aunSp3nvvPZWXl/vVnaoZMwMAgPV4fczM2Zo2baqwsDA1adJElZWVntglAACAS0yXGbvdrqeeekpdu3ZV7969tXXrVqWlpamgoMCT+QAAAM7J1KR511xzjTIzM9WjRw/de++9uv3223XJJZd4OhsAAMB5mSozN9xwg15//XUlJCR4Og8AAIBb3D7NVFlZqXfeeUc2GzcjBAAAvud2mQkODlZ5ebk3sgAAALjN1ADg5ORkzZ07VydPnvR0HgAAALeYGjOTmZmptWvX6rPPPlP37t3VtGnTaus/+OADj4QDAAA4H1Nlpnnz5rr11ls9nQUAAMBtpsrMG2+84ekcAAAAppieNO/kyZNas2aNXnnlFR09elSStH//fpWWlnosHAAAwPmYOjKzZ88eDRs2THv37lVFRYWGDBmi8PBwzZ07VxUVFVq0aJGncwIAANTK1JGZKVOmqHfv3jpy5IjCwsKcy3/zm99o7dq1HgsHAABwPqaOzHzxxRf66quv1KRJk2rLO3TooH379nkkGAAAgCtMHZlxOByqqqqqsfznn39WeHh4vUMBAAC4ylSZGTp0qObPn+98brPZVFpaqtTUVN14442eygYAAHBeNsMwDHdf9PPPPyspKUmGYejHH39U79699eOPP+riiy/Whg0bFB0d7Y2sppSUlCgyMlLFxcWKiIjwdRwAAOACdz6/TZUZ6dSl2e+++662bdum0tJS9erVS3fccUe1AcH+gDIDAID1XJAyYxWUGQAArMedz29TY2befPNNffLJJ87nDz30kJo3b65+/fppz549ZnYJAABgiqky8/TTTztPJ23atEkvvviinnnmGV188cWaNm2aRwMCAACci6l5ZvLy8tSpUydJ0ocffqjf/va3mjBhgvr376/rrrvOk/kAAADOydSRmWbNmunw4cOSpM8++0xDhgyRJIWGhur48eOeSwcAAHAepo7MDBkyRH/4wx+UmJionTt3OueW+f7779WhQwdP5gMAADgnU0dmXnrpJfXt21eHDh3S+++/r5YtW0qStmzZottvv92jAQEAAM6FS7MBAIDfcefz29RpJkk6cuSI/v73vys3N1eS1KVLF40bN05RUVFmdwkAAOA2U6eZNmzYoA4dOmjBggU6cuSIjhw5ohdeeEHx8fHasGGDpzMCAADUydRppu7du6tv375auHChAgMDJUlVVVV64IEH9NVXX+m7777zeFCzOM0EAID1eH0G4F27dmn69OnOIiNJgYGBSklJ0a5du8zsEgAAwBRTZaZXr17OsTJnys3NVc+ePesdCgAAwFUuDwD+9ttvnX+ePHmypkyZol27dumaa66RJH399dd66aWXNGfOHM+nBAAAqIPLY2YCAgJks9l0vs1tNpuqqqo8Es4TGDMDAID1eGXMjN1u108//SS73X7Ox08//eRy0A0bNmjEiBGKjY2VzWbThx9+WOe2999/v2w2m+bPn+/y/tF4VDkMbdp9WB9l79Om3YdV5WjQ0ycBAM7g8mmm9u3be/yLl5WVqWfPnho3bpxGjRpV53bLli3T119/rdjYWI9ngPWt3J6vtOU5yi8udy6LiQxV6ogEDesW48NkAIALwfSkebt379b8+fOdA4ETEhI0ZcoUdezY0eV9DB8+XMOHDz/nNvv27dODDz6oVatW6aabbjIbFw3Uyu35mrgkS2cfhykoLtfEJVlaeGcvCg0ANHCmrmZatWqVEhISlJGRoR49eqhHjx7avHmzunbtqtWrV3ssnMPh0F133aUZM2aoa9euHtsvGoYqh6G05Tk1iowk57K05TmccgKABs7UkZmZM2dq2rRpNa5cmjlzph5++GENGTLEI+Hmzp2roKAgTZ482eXXVFRUqKKiwvm8pKTEI1ngfzLshdVOLZ3NkJRfXK4Me6H6dmx54YIBAC4oU0dmcnNzNX78+BrLx40bp5ycnHqHkk7dgfv555/X4sWLZbPZXH7d7NmzFRkZ6XzExcV5JA/8z8GjdRcZM9sBAKzJVJlp1aqVsrOzayzPzs5WdHR0fTNJkr744gsdPHhQ7dq1U1BQkIKCgrRnzx5Nnz5dHTp0qPN1s2bNUnFxsfORl5fnkTzwP9HhoR7dDgBgTaZOM913332aMGGCfvrpJ/Xr10+StHHjRs2dO1cpKSkeCXbXXXdp8ODB1ZYlJSXprrvu0r333lvn60JCQhQSEuKRDPBvfeKjFBMZqoLi8lrHzdgktYkMVZ947uQOAA2ZqTLz2GOPKTw8XPPmzdOsWbMkSbGxsXriiSfcGt9SWlpa7V5Odrtd2dnZioqKUrt27dSyZfVxDsHBwWrTpo0uu+wyM7HRwAQG2JQ6IkETl2TJJlUrNKdPTKaOSFBggOunKQEA1mPqrtlnOnr0qCQpPDzc7deuX79egwYNqrF87NixWrx4cY3lHTp00NSpUzV16lSXvwYzADd8zDMDAA2PO5/f9S4z/o4y0zhUOQxl2At18Gi5osNPnVriiAwAWJc7n9+mJ80D/ElggI3LrwGgkTJ1NRMAAIC/oMwAAABLM1Vm/vGPf1SbZfe0EydO6B//+Ee9QwEAALjK1ADgwMBA5efn15gg7/Dhw4qOjlZVVZXHAtYXA4ABALAedz6/TR2ZMQyj1lsM/Pzzz4qMjDSzSwAAAFPcupopMTFRNptNNptNN9xwg4KC/u/lVVVVstvtGjZsmMdDAgAA1MWtMjNy5EhJp+7BlJSUpGbNmjnXNWnSRB06dNCtt97q0YAAAADn4laZSU1NlXRqJt4xY8ZwDyQAAOBzpsbMJCQk1HrX7M2bN+ubb76pbyYAAACXmSozycnJysvLq7F83759Sk5OrncoAAAAV5kqMzk5OerVq1eN5YmJicrJyal3KAAAAFeZKjMhISE6cOBAjeX5+fnVrnACAADwNlNlZujQoZo1a5aKi4udy4qKivTII49oyJAhHgsHAABwPqYOozz77LMaOHCg2rdvr8TEREmnLtdu3bq13nrrLY8GBBqTKoehDHuhDh4tV3R4qPrERykwoOYElYA7+L7yL7wfnmeqzFxyySX69ttv9fbbb2vbtm0KCwvTvffeq9tvv13BwcGezgg0Ciu35ytteY7yi8udy2IiQ5U6IkHDusX4MBmsjO8r/8L74R2m7s1kJdybCVawcnu+Ji7J0tk/jKf/X23hnb34RQe38X3lX3g/3OP1ezNJ0ltvvaUBAwYoNjZWe/bskSQ999xz+uijj8zuEmiUqhyG0pbn1PgFJ8m5LG15jqocDfr/O+BhfF/5F94P7zJVZhYuXKiUlBQNHz5cR44ccd4lu0WLFpo/f74n8wENXoa9sNoh57MZkvKLy5VhL7xwoWB5fF/5F94P7zJVZl544QW99tprevTRR6tdit27d2999913HgsHNAYHj9b9C87MdoDE95W/4f3wLlNlxm63O69iOlNISIjKysrqHQpoTKLDQz26HSDxfeVveD+8y1SZiY+Pr/XeTCtXrlSXLl3qmwloVPrERykmMlR1XZhp06mrHfrER13IWLA4vq/8C++Hd5kqMykpKUpOTta7774rwzCUkZGhp556SrNmzdJDDz3k6YxAgxYYYFPqiARJqvGL7vTz1BEJzEMBt/B95V94P7zL9KXZb7/9tp544gnt3r1bkhQbG6u0tDSNHz/eowHri0uzYRXMPwFv4PvKv/B+uM6dz2+3y8zJkye1dOlSJSUlqXXr1jp27JhKS0sVHR1dr9DeQpmBlTAzKLyB7yv/wvvhGq+WGUm66KKLlJubq/bt25sOeaFQZgAAsB6vT5rXp08fbd261VQ4AAAATzJ1b6YHHnhA06dP188//6wrr7xSTZs2rba+R48eHgkHAABwPqZOMwUE1DygY7PZZBiGbDabc0Zgf8BpJgAArMedz29TR2bsdrupYAAAAJ7mdpmprKzU9ddfrxUrVjBBHgAA8Dm3BwAHBwervJx7RwAAAP9g6mqm5ORkzZ07VydPnvR0HgAAALeYGjOTmZmptWvX6rPPPlP37t1rXM30wQcfeCQcAADA+ZgqM82bN9ett97q6SwAAABuM1Vm3njjDU/nAAAAMMVUmTnt0KFD2rFjhyTpsssuU6tWrTwSCgAAwFWmBgCXlZVp3LhxiomJ0cCBAzVw4EDFxsZq/PjxOnbsmKczAgAA1MlUmUlJSVF6erqWL1+uoqIiFRUV6aOPPlJ6erqmT5/u6YwAAAB1MnU7g4svvlj//ve/dd1111Vbvm7dOo0ePVqHDh3yVL5643YGAABYj9fvmn3s2DG1bt26xvLo6GhOMwEAgAvKVJnp27evUlNTq80EfPz4caWlpalv374u72fDhg0aMWKEYmNjZbPZ9OGHHzrXVVZW6uGHH3bOYxMbG6u7775b+/fvNxMZAAA0UKauZnr++eeVlJSktm3bqmfPnpKkbdu2KTQ0VKtWrXJ5P2VlZerZs6fGjRunUaNGVVt37NgxZWVl6bHHHlPPnj115MgRTZkyRTfffLO++eYbM7EBAEADZGrMjHSqbLz99tv64YcfJEldunTRHXfcobCwMHNBbDYtW7ZMI0eOrHObzMxM9enTR3v27FG7du1c2i9jZgAAsB53Pr9NzzNz0UUX6b777jP7clOKi4tls9nUvHnzOrepqKhQRUWF83lJSckFSAYAAHzF1JiZ2bNn6/XXX6+x/PXXX9fcuXPrHao25eXlevjhh3X77befs6HNnj1bkZGRzkdcXJxX8gAAAP9gqsy88soruvzyy2ss79q1qxYtWlTvUGerrKzU6NGjZRiGFi5ceM5tZ82apeLiYucjLy/P43kAAID/MHWaqaCgQDExMTWWt2rVSvn5+fUOdabTRWbPnj36/PPPz3veLCQkRCEhIR7NAAAA/JepIzNxcXHauHFjjeUbN25UbGxsvUOddrrI/Pjjj1qzZo1atmzpsX0DAICGwdSRmfvuu09Tp05VZWWlrr/+eknS2rVr9dBDD7l1O4PS0lLt2rXL+dxutys7O1tRUVGKiYnRb3/7W2VlZWnFihWqqqpSQUGBJCkqKkpNmjQxEx0AADQwpi7NNgxDM2fO1IIFC3TixAlJUmhoqB5++GE9/vjjLu9n/fr1GjRoUI3lY8eO1RNPPKH4+PhaX7du3boat1KoC5dmAwBgPe58fpueZ0Y6dWQlNzdXYWFhuvTSS/1yrAplBgAA67kg88xIUrNmzXTVVVfVZxcAAAD1YmoAMAAAgL+gzAAAAEujzAAAAEtzucz06tVLR44ckST96U9/0rFjx7wWCgAAwFUul5nc3FyVlZVJktLS0lRaWuq1UAAAAK5y+WqmK664Qvfee68GDBggwzD07LPPqlmzZrVu685cMwAAAPXh8jwzO3bsUGpqqnbv3q2srCwlJCQoKKhmF7LZbMrKyvJ4ULOYZwYAAOvx+qR5AQEBKigoUHR0tOmQFwplBgAA6/H6pHkOh8NUMAAAAE8zPQPw7t27NX/+fOXm5kqSEhISNGXKFHXs2NFj4QAAAM7H1Dwzq1atUkJCgjIyMtSjRw/16NFDmzdvVteuXbV69WpPZwQAAKiTqTEziYmJSkpK0pw5c6otnzlzpj777DMGAAMAgHpx5/Pb1JGZ3NxcjR8/vsbycePGKScnx8wuAQAATDFVZlq1aqXs7Oway7Ozsy1xhRMAAGg4TA0Avu+++zRhwgT99NNP6tevnyRp48aNmjt3rlJSUjwaEAAA4FxMjZkxDEPz58/XvHnztH//fklSbGysZsyYocmTJ8tms3k8qFmMmQEAwHq8PmnemY4ePSpJCg8Pr89uvIYyAwCA9Xh90rwz+WuJAQAAjYOpAcAAAAD+gjIDAAAsjTIDAAAsjTIDAAAszXSZmTRpkgoLCz2ZBQAAwG1ulZmff/7Z+eelS5eqtLRUktS9e3fl5eV5NhkAAIAL3Lo0+/LLL1fLli3Vv39/lZeXKy8vT+3atdN//vMfVVZWeisjAABAndw6MlNUVKT33ntPV155pRwOh2688UZ17txZFRUVWrVqlQ4cOOCtnAAAALVyawbg48ePKywsTJLUokULbdmyRfn5+Ro8eLC6deum77//XnFxcdqxY4fXAruLGYABALAer80A3Lx5c11xxRXq37+/Tpw4oePHj6t///4KCgrSu+++q0suuUSZmZn1Cg8AAOAOt04z7du3T//v//0/hYSE6OTJk7ryyiv1X//1Xzpx4oSysrJks9k0YMAAb2UFAACowfSNJlu0aKENGzYoNzdXd999t9q0aaMDBw6oT58+Sk9P93RO0zjNBACA9bjz+V2vSfMiIyM1evRoBQcH6/PPP5fdbtcDDzxQn10CAAC4xfRds7/99ltdcsklkqT27dsrODhYbdq00W233eaxcAAAAOdjuszExcU5/7x9+3aPhAEAAHAX92YCAACWRpkBAACWRpkBAACWRpkBAACWRpkBAACWRpkBAACW5tMys2HDBo0YMUKxsbGy2Wz68MMPq603DEOPP/64YmJiFBYWpsGDB+vHH3/0TVgAgE9VOQxt2n1YH2Xv06bdh1XlMDWBPRog0/PMeEJZWZl69uypcePGadSoUTXWP/PMM1qwYIHefPNNxcfH67HHHlNSUpJycnIUGhrqg8QAAF9YuT1factzlF9c7lwWExmq1BEJGtYtxofJ4A9M35vJ02w2m5YtW6aRI0dKOnVUJjY2VtOnT9cf//hHSVJxcbFat26txYsXa8yYMS7tl3szAYC1rdyer4lLsnT2h5Xtf/+78M5eFJoG6ILdm8mb7Ha7CgoKNHjwYOeyyMhIXX311dq0aZMPkwEALpQqh6G05Tk1iowk57K05Tmccmrk/LbMFBQUSJJat25dbXnr1q2d62pTUVGhkpKSag8AgDVl2AurnVo6myEpv7hcGfbCCxcKfsdvy4xZs2fPVmRkpPNx5j2kAADWcvBo3UXGzHZomPy2zLRp00aSdODAgWrLDxw44FxXm1mzZqm4uNj5yMvL82pOAID3RIe7drGHq9uhYfLbMhMfH682bdpo7dq1zmUlJSXavHmz+vbtW+frQkJCFBERUe0BALCmPvFRiokMdQ72PZtNp65q6hMfdSFjwc/4tMyUlpYqOztb2dnZkk4N+s3OztbevXtls9k0depUPfnkk/r444/13Xff6e6771ZsbKzziicAQMMWGGBT6ogESapRaE4/Tx2RoMCAuuoOGgOfXpq9fv16DRo0qMbysWPHavHixTIMQ6mpqXr11VdVVFSkAQMG6OWXX1bnzp1d/hpcmg0A1sc8M42PO5/ffjPPjLdQZgCgYahyGMqwF+rg0XJFh586tcQRmYbLnc9vn84ADACAqwIDbOrbsaWvY8AP+e0AYAAAAFdQZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKVRZgAAgKUF+ToAAPirKoehDHuhDh4tV3R4qPrERykwwObrWADOQpkBgFqs3J6vtOU5yi8udy6LiQxV6ogEDesW48NkAM7GaSYAOMvK7fmauCSrWpGRpILick1ckqWV2/N9lAxAbSgzAHCGKoehtOU5MmpZd3pZ2vIcVTlq2wKAL1BmAOAMGfbCGkdkzmRIyi8uV4a98MKFAnBOlBkAOMPBo3UXGTPbAfA+ygwAnCE6PNSj2wHwPsoMAJyhT3yUYiJDVdcF2DaduqqpT3zUhYwF4BwoMwBwhsAAm1JHJEhSjUJz+nnqiATmmwH8CGUGAM4yrFuMFt7ZS20iq59KahMZqoV39mKeGcDPMGkeANRiWLcYDUlowwzAgAVQZgCgDoEBNvXt2NLXMQCcB6eZAACApVFmAACApVFmAACApVFmAACApVFmAACApVFmAACApVFmAACApVFmAACApVFmAACApTX4GYANw5AklZSU+DgJAABw1enP7dOf4+fS4MvM0aNHJUlxcXE+TgIAANx19OhRRUZGnnMbm+FK5bEwh8Oh/fv3Kzw8XDYbN4irTUlJieLi4pSXl6eIiAhfx2n0eD/8C++Hf+H98C/efD8Mw9DRo0cVGxurgIBzj4pp8EdmAgIC1LZtW1/HsISIiAh+OfgR3g//wvvhX3g//Iu33o/zHZE5jQHAAADA0igzAADA0igzUEhIiFJTUxUSEuLrKBDvh7/h/fAvvB/+xV/ejwY/ABgAADRsHJkBAACWRpkBAACWRpkBAACWRplppGbPnq2rrrpK4eHhio6O1siRI7Vjxw5fx8L/mjNnjmw2m6ZOnerrKI3avn37dOedd6ply5YKCwtT9+7d9c033/g6VqNUVVWlxx57TPHx8QoLC1PHjh315z//2aWp7lF/GzZs0IgRIxQbGyubzaYPP/yw2nrDMPT4448rJiZGYWFhGjx4sH788ccLlo8y00ilp6crOTlZX3/9tVavXq3KykoNHTpUZWVlvo7W6GVmZuqVV15Rjx49fB2lUTty5Ij69++v4OBgffrpp8rJydG8efPUokULX0drlObOnauFCxfqxRdfVG5urubOnatnnnlGL7zwgq+jNQplZWXq2bOnXnrppVrXP/PMM1qwYIEWLVqkzZs3q2nTpkpKSlJ5efkFycfVTJAkHTp0SNHR0UpPT9fAgQN9HafRKi0tVa9evfTyyy/rySef1BVXXKH58+f7OlajNHPmTG3cuFFffPGFr6NA0q9//Wu1bt1af//7353Lbr31VoWFhWnJkiU+TNb42Gw2LVu2TCNHjpR06qhMbGyspk+frj/+8Y+SpOLiYrVu3VqLFy/WmDFjvJ6JIzOQdOobT5KioqJ8nKRxS05O1k033aTBgwf7Okqj9/HHH6t379763e9+p+joaCUmJuq1117zdaxGq1+/flq7dq127twpSdq2bZu+/PJLDR8+3MfJYLfbVVBQUO33VmRkpK6++mpt2rTpgmRo8Pdmwvk5HA5NnTpV/fv3V7du3Xwdp9F65513lJWVpczMTF9HgaSffvpJCxcuVEpKih555BFlZmZq8uTJatKkicaOHevreI3OzJkzVVJSossvv1yBgYGqqqrSU089pTvuuMPX0Rq9goICSVLr1q2rLW/durVznbdRZqDk5GRt375dX375pa+jNFp5eXmaMmWKVq9erdDQUF/HgU6V/N69e+vpp5+WJCUmJmr79u1atGgRZcYH/vWvf+ntt9/W0qVL1bVrV2VnZ2vq1KmKjY3l/QCnmRq7SZMmacWKFVq3bh13F/ehLVu26ODBg+rVq5eCgoIUFBSk9PR0LViwQEFBQaqqqvJ1xEYnJiZGCQkJ1ZZ16dJFe/fu9VGixm3GjBmaOXOmxowZo+7du+uuu+7StGnTNHv2bF9Ha/TatGkjSTpw4EC15QcOHHCu8zbKTCNlGIYmTZqkZcuW6fPPP1d8fLyvIzVqN9xwg7777jtlZ2c7H71799Ydd9yh7OxsBQYG+jpio9O/f/8a0xXs3LlT7du391Gixu3YsWMKCKj+kRUYGCiHw+GjRDgtPj5ebdq00dq1a53LSkpKtHnzZvXt2/eCZOA0UyOVnJyspUuX6qOPPlJ4eLjzvGZkZKTCwsJ8nK7xCQ8PrzFeqWnTpmrZsiXjmHxk2rRp6tevn55++mmNHj1aGRkZevXVV/Xqq6/6OlqjNGLECD311FNq166dunbtqq1bt+qvf/2rxo0b5+tojUJpaal27drlfG6325Wdna2oqCi1a9dOU6dO1ZNPPqlLL71U8fHxeuyxxxQbG+u84snrDDRKkmp9vPHGG76Ohv917bXXGlOmTPF1jEZt+fLlRrdu3YyQkBDj8ssvN1599VVfR2q0SkpKjClTphjt2rUzQkNDjV/96lfGo48+alRUVPg6WqOwbt26Wj8zxo4daxiGYTgcDuOxxx4zWrdubYSEhBg33HCDsWPHjguWj3lmAACApTFmBgAAWBplBgAAWBplBgAAWBplBgAAWBplBgAAWBplBgAAWBplBgAAWBplBgAAWBplBoBXLV68WM2bN/d1jBruueeeCzfVOgCvoswAOKd77rlHNptNc+bMqbb8ww8/lM1mO+/rb7vtNu3cudP5/IknntAVV1zh6Zh1+s9//iObzabs7Oxqy59//nktXrz4guUA4D2UGQDnFRoaqrlz5+rIkSNuvzYsLEzR0dEez3TixIl6vT4yMtIvjxgBcB9lBsB5DR48WG3atNHs2bPdfu2Zp5kWL16stLQ0bdu2TTabTTabzXl0pKioSH/4wx/UqlUrRURE6Prrr9e2bduc+zl9ROdvf/ub4uPjFRoaKklauXKlBgwYoObNm6tly5b69a9/rd27dztfFx8fL0lKTEyUzWbTddddJ6nmaaaKigpNnjxZ0dHRCg0N1YABA5SZmelcv379etlsNq1du1a9e/fWRRddpH79+mnHjh3ObbZt26ZBgwYpPDxcERERuvLKK/XNN9+4/W8GwD2UGQDnFRgYqKefflovvPCCfv75Z9P7ue222zR9+nR17dpV+fn5ys/P12233SZJ+t3vfqeDBw/q008/1ZYtW9SrVy/dcMMNKiwsdL5+165dev/99/XBBx84TxuVlZUpJSVF33zzjdauXauAgAD95je/kcPhkCRlZGRIktasWaP8/Hx98MEHtWZ76KGH9P777+vNN99UVlaWOnXqpKSkpGpfX5IeffRRzZs3T998842CgoI0btw457o77rhDbdu2VWZmprZs2aKZM2cqODjY9L8XABddsPtzA7CksWPHGrfccothGIZxzTXXGOPGjTMMwzCWLVtmuPIr5I033jAiIyOdz1NTU42ePXtW2+aLL74wIiIijPLy8mrLO3bsaLzyyivO1wUHBxsHDx4859c7dOiQIcn47rvvDMMwDLvdbkgytm7dWuffq7S01AgODjbefvtt5/oTJ04YsbGxxjPPPGMYhmGsW7fOkGSsWbPGuc0nn3xiSDKOHz9uGIZhhIeHG4sXLz73PwgAj+PIDACXzZ07V2+++aZyc3M9ut9t27aptLRULVu2VLNmzZwPu91e7ZRR+/bt1apVq2qv/fHHH3X77bfrV7/6lSIiItShQwdJ0t69e13++rt371ZlZaX69+/vXBYcHKw+ffrU+Lv26NHD+eeYmBhJ0sGDByVJKSkp+sMf/qDBgwdrzpw51bID8B7KDACXDRw4UElJSZo1a5ZH91taWqqYmBhlZ2dXe+zYsUMzZsxwbte0adMarx0xYoQKCwv12muvafPmzdq8ebOk+g8QrsuZp41OX811+pTWE088oe+//1433XSTPv/8cyUkJGjZsmVeyQHg/wT5OgAAa5kzZ46uuOIKXXbZZaZe36RJE1VVVVVb1qtXLxUUFCgoKMh5ZMUVhw8f1o4dO/Taa6/pv/7rvyRJX375ZY2vJ6nG1zxTx44d1aRJE23cuFHt27eXJFVWViozM1NTp051OY8kde7cWZ07d9a0adN0++2364033tBvfvMbt/YBwD0cmQHglu7du+uOO+7QggULTL2+Q4cOstvtys7O1i+//KKKigoNHjxYffv21ciRI/XZZ5/pP//5j7766is9+uij57waqEWLFmrZsqVeffVV7dq1S59//rlSUlKqbRMdHa2wsDCtXLlSBw4cUHFxcY39NG3aVBMnTtSMGTO0cuVK5eTk6L777tOxY8c0fvx4l/5ex48f16RJk7R+/Xrt2bNHGzduVGZmprp06eLePxAAt1FmALjtT3/6k/PUirtuvfVWDRs2TIMGDVKrVq30z3/+UzabTf/zP/+jgQMH6t5771Xnzp01ZswY7dmzR61bt65zXwEBAXrnnXe0ZcsWdevWTdOmTdNf/vKXatsEBQVpwYIFeuWVVxQbG6tbbrml1n3NmTNHt956q+666y716tVLu3bt0qpVq9SiRQuX/l6BgYE6fPiw7r77bnXu3FmjR4/W8OHDlZaW5vo/DgBTbIZhGL4OAQAAYBZHZgAAgKVRZgDUy/Dhw6tdTn3m4+mnn/Z1PACNAKeZANTLvn37dPz48VrXRUVFKSoq6gInAtDYUGYAAIClcZoJAABYGmUGAABYGmUGAABYGmUGAABYGmUGAABYGmUGAABYGmUGAABYGmUGAABY2v8H1oW+TPSOUWMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -967,7 +967,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d59c96f",
+   "id": "137c056e",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -979,7 +979,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "213634c8",
+   "id": "e98c163d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -991,7 +991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c2fcafbc",
+   "id": "2078f0ed",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1009,7 +1009,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "17c9b3a1",
+   "id": "5465c5fe",
    "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": "5eec6ebb",
+   "id": "d05c118d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/grover_number_light.ipynb b/_sources/grover_number_light.ipynb
index e76db77..652ef51 100644
--- a/_sources/grover_number_light.ipynb
+++ b/_sources/grover_number_light.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2b6971ca",
+   "id": "76c0b3fe",
    "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": "e851cf4d",
+   "id": "1afd32dd",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1b38c9d2",
+   "id": "0f415651",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -77,7 +77,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "92aa0285",
+   "id": "bf6dd222",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -96,7 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "31b93387",
+   "id": "3ad241b3",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92eccad2",
+   "id": "f81954d4",
    "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": "a4c36b17",
+   "id": "0dbf85f6",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -157,7 +157,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33dfde3e",
+   "id": "78bc7f08",
    "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": "94e3e8cd",
+   "id": "9a46d31f",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -211,7 +211,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2fb3f1f",
+   "id": "461460f6",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/more_dynamics.ipynb b/_sources/more_dynamics.ipynb
index ba7ad58..dc5f391 100644
--- a/_sources/more_dynamics.ipynb
+++ b/_sources/more_dynamics.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e0c41d81",
+   "id": "28068798",
    "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": "c5e28697",
+   "id": "f5b14e06",
    "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": "3f2b9f20",
+   "id": "69aae66f",
    "metadata": {},
    "source": [
     "### 問題\n",
@@ -44,7 +44,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "f609e044",
+   "id": "4e13c2ab",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -66,7 +66,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "1cbaf48e",
+   "id": "f7f7a250",
    "metadata": {
     "tags": [
      "remove-output"
@@ -75,7 +75,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "983ab428",
+   "id": "e2bd1cae",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3fcdaab8",
+   "id": "d4b495b3",
    "metadata": {},
    "source": [
     "### おまけ: スピン総和\n",
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ca34466a",
+   "id": "92c340fc",
    "metadata": {},
    "source": [
     "## 問題2: シュウィンガーモデル\n",
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "74259b3c",
+   "id": "61efd869",
    "metadata": {},
    "source": [
     "### シュウィンガーモデルの物理\n",
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32e3c9f9",
+   "id": "810a390f",
    "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": "b0373ffe",
+   "id": "4b44b4a2",
    "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": "40a4e798",
+   "id": "797d8035",
    "metadata": {},
    "source": [
     "### 問題\n",
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6d3d66da",
+   "id": "86370c86",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -442,7 +442,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c249a425",
+   "id": "64acc7bf",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -453,7 +453,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f1f6bf1d750>]"
+       "[<matplotlib.lines.Line2D at 0x7f9ac3f46dd0>]"
       ]
      },
      "execution_count": 4,
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc0bd3f3",
+   "id": "95e184a1",
    "metadata": {
     "tags": [
      "remove-output"
diff --git a/_sources/numerics.ipynb b/_sources/numerics.ipynb
index 0a1d659..444ef87 100644
--- a/_sources/numerics.ipynb
+++ b/_sources/numerics.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0e6bce2f",
+   "id": "a904efda",
    "metadata": {},
    "source": [
     "# 予備知識：数値表現\n",
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f5b6376",
+   "id": "f1dd8d23",
    "metadata": {},
    "source": [
     "(signed_binary)=\n",
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "10288053",
+   "id": "06cf589e",
    "metadata": {},
    "source": [
     "(nonintegral_fourier)=\n",
@@ -61,13 +61,13 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "d77e0c7e",
+   "id": "e7b2e227",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f1842f71d80>"
+       "<matplotlib.legend.Legend at 0x7f36c838dd20>"
       ]
      },
      "execution_count": 1,
diff --git a/_sources/prerequisites.ipynb b/_sources/prerequisites.ipynb
index 59df1f1..b5cc6cd 100644
--- a/_sources/prerequisites.ipynb
+++ b/_sources/prerequisites.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "a878fc32",
+   "id": "ec545bb2",
    "metadata": {},
    "source": [
     "# 実習の準備\n",
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c13b42cf",
+   "id": "ff31c140",
    "metadata": {},
    "source": [
     "## IBM Quantum\n",
@@ -42,7 +42,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a0f89fbe",
+   "id": "5e9ff24d",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4dee9e67",
+   "id": "10f3541f",
    "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 3f49268..219f088 100644
--- a/_sources/python_intro.ipynb
+++ b/_sources/python_intro.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c68bf64f",
+   "id": "b4a22605",
    "metadata": {},
    "source": [
     "# 予備知識：Python\n",
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9681fe3b",
+   "id": "abe397cb",
    "metadata": {},
    "source": [
     "## 基礎編\n",
@@ -39,7 +39,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "ea396fd2",
+   "id": "2e86f6c0",
    "metadata": {
     "tags": [
      "remove-output"
@@ -54,7 +54,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3085bbb2",
+   "id": "34ccbb25",
    "metadata": {
     "tags": [
      "remove-output"
@@ -69,7 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "79bdddee",
+   "id": "49982592",
    "metadata": {
     "tags": [
      "remove-output"
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4357ca79",
+   "id": "e81fb1b6",
    "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": "2021091d",
+   "id": "0a3a647a",
    "metadata": {
     "tags": [
      "remove-output"
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f8fba074",
+   "id": "529dadae",
    "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": "6b4e9e79",
+   "id": "0c3b733b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a553af30",
+   "id": "34448ec1",
    "metadata": {},
    "source": [
     "```{admonition} インデンテーション\n",
@@ -192,7 +192,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0a028f2f",
+   "id": "a4c003f0",
    "metadata": {},
    "source": [
     "### 変数\n",
@@ -207,7 +207,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cc3a82fd",
+   "id": "7c3872ef",
    "metadata": {
     "tags": [
      "remove-output"
@@ -233,7 +233,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "fbb8a305",
+   "id": "398938dd",
    "metadata": {
     "tags": [
      "remove-output"
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16835012",
+   "id": "ba532c17",
    "metadata": {},
    "source": [
     "```{admonition} 文字列\n",
@@ -262,7 +262,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "5ef6c400",
+   "id": "f5dde533",
    "metadata": {
     "tags": [
      "remove-output"
@@ -278,7 +278,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "33ac948b",
+   "id": "6644fe3b",
    "metadata": {},
    "source": [
     "### 演算子\n",
@@ -289,7 +289,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "7936a067",
+   "id": "3e5dc268",
    "metadata": {
     "tags": [
      "remove-output"
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "703f0db3",
+   "id": "57b7a468",
    "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": "61a68975",
+   "id": "a2314d25",
    "metadata": {
     "tags": [
      "remove-output"
@@ -416,7 +416,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be399d91",
+   "id": "fb557102",
    "metadata": {},
    "source": [
     "### 基本のデータ型\n",
@@ -433,7 +433,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d97a54b5",
+   "id": "572e01e1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -471,7 +471,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd83139f",
+   "id": "5e8abab8",
    "metadata": {},
    "source": [
     "#### 文字列型 str\n",
@@ -482,7 +482,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "027ff645",
+   "id": "75b3ca2c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -503,7 +503,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5262cac9",
+   "id": "006002c1",
    "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": "4d1df8bc",
+   "id": "aca02a83",
    "metadata": {
     "tags": [
      "remove-output"
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "063419ad",
+   "id": "f5027b97",
    "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": "4cfb85a5",
+   "id": "ff20ab51",
    "metadata": {
     "tags": [
      "remove-output"
@@ -566,7 +566,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fb0dc9cc",
+   "id": "d08c3f95",
    "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": "7a8af295",
+   "id": "0db1fc66",
    "metadata": {
     "tags": [
      "remove-output"
@@ -607,7 +607,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20dd7a33",
+   "id": "4016385c",
    "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": "803d80cd",
+   "id": "e1ae8b08",
    "metadata": {
     "tags": [
      "remove-output"
@@ -637,7 +637,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d963ca7",
+   "id": "68112ced",
    "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": "2f98b3af",
+   "id": "a705d684",
    "metadata": {
     "tags": [
      "remove-output"
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "96e5e30f",
+   "id": "2e330b73",
    "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": "07bfd855",
+   "id": "f4273a76",
    "metadata": {
     "tags": [
      "remove-output",
@@ -706,7 +706,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "211eae00",
+   "id": "9fc54cdc",
    "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": "8eb29a07",
+   "id": "f4aad9d1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -737,7 +737,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bb420869",
+   "id": "773bed14",
    "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": "4733597c",
+   "id": "90f8b416",
    "metadata": {
     "tags": [
      "remove-output"
@@ -767,7 +767,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ff342c58",
+   "id": "f9a072c5",
    "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": "116c6f83",
+   "id": "7c64ea21",
    "metadata": {
     "tags": [
      "remove-output"
@@ -799,7 +799,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a56ed38d",
+   "id": "319641fe",
    "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": "37f5e32c",
+   "id": "1bf2bb02",
    "metadata": {
     "tags": [
      "remove-output"
@@ -833,7 +833,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a987fea",
+   "id": "0f39758f",
    "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": "f3aa92e8",
+   "id": "f62cfd04",
    "metadata": {
     "tags": [
      "remove-output"
@@ -867,7 +867,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed96d4cb",
+   "id": "814c7307",
    "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": "fc5563b0",
+   "id": "2b825f50",
    "metadata": {
     "tags": [
      "remove-output"
@@ -901,7 +901,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8a69a52d",
+   "id": "3610d383",
    "metadata": {},
    "source": [
     "#### 配列型 tuple, list\n",
@@ -912,7 +912,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "593408f5",
+   "id": "72ef135f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -938,7 +938,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b7dbf8ec",
+   "id": "a2865665",
    "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": "7abf8e84",
+   "id": "9104f4cf",
    "metadata": {
     "tags": [
      "remove-output"
@@ -973,7 +973,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91d1ee96",
+   "id": "abb6bd3e",
    "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": "58888fa0",
+   "id": "b8255e87",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1006,7 +1006,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d6a51594",
+   "id": "364bfd13",
    "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": "29afa3cb",
+   "id": "ac6648e4",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1040,7 +1040,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec874902",
+   "id": "c5b4f889",
    "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": "26c65303",
+   "id": "f9407ade",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f9f17bdc",
+   "id": "dfe0172e",
    "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": "9ea59679",
+   "id": "55f277df",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe937711",
+   "id": "26f5d8b5",
    "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": "80dd24fb",
+   "id": "fbce1c97",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1145,7 +1145,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "743bc481",
+   "id": "c96c48b0",
    "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": "b93ad3c8",
+   "id": "10628a3f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1176,7 +1176,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1b74d7f",
+   "id": "ced95c00",
    "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": "4e554f23",
+   "id": "fe63315c",
    "metadata": {},
    "source": [
     "#### 辞書型 dict\n",
@@ -1195,7 +1195,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "a337bc67",
+   "id": "4abcb0da",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1213,7 +1213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3d6c68b",
+   "id": "5d6e589d",
    "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": "6316e41a",
+   "id": "a8d7cfb7",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1247,7 +1247,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fe38cdb",
+   "id": "c32d0da5",
    "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": "d60dba2f",
+   "id": "935f4589",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1277,7 +1277,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e5dafaca",
+   "id": "576b66c5",
    "metadata": {},
    "source": [
     "#### 真理値型 bool\n",
@@ -1288,7 +1288,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "c3889f22",
+   "id": "deaca39c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1340,7 +1340,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ce976cb9",
+   "id": "bede6da6",
    "metadata": {},
    "source": [
     "#### None型\n",
@@ -1350,7 +1350,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "07c1acf2",
+   "id": "01fbb84a",
    "metadata": {},
    "source": [
     "### 関数\n",
@@ -1369,7 +1369,7 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "id": "469c26d1",
+   "id": "49665f96",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1400,7 +1400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0d2b55f1",
+   "id": "bc07026d",
    "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": "96097ed5",
+   "id": "0ae6901b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1455,7 +1455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c28bf47c",
+   "id": "4a5703d6",
    "metadata": {},
    "source": [
     "### if文（条件文）\n",
@@ -1466,7 +1466,7 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "id": "b18d03cd",
+   "id": "69453914",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1494,7 +1494,7 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "id": "f66bea8e",
+   "id": "810d824d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1517,7 +1517,7 @@
   {
    "cell_type": "code",
    "execution_count": 41,
-   "id": "c86f53cd",
+   "id": "e50d2d79",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -1544,7 +1544,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f06ed574",
+   "id": "6095d317",
    "metadata": {},
    "source": [
     "```{admonition} 変数のスコープ\n",
@@ -1557,7 +1557,7 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "id": "f28a8199",
+   "id": "37effecc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1584,7 +1584,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "24dd50cc",
+   "id": "63bdd8da",
    "metadata": {},
    "source": [
     "### for文、while文（ループ）\n",
@@ -1595,7 +1595,7 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "id": "b2a2efb1",
+   "id": "c42c18a9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1646,7 +1646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69c4fa29",
+   "id": "8409d6cb",
    "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": "cc93f1b2",
+   "id": "278e1d46",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1673,7 +1673,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "09e55c1c",
+   "id": "814c02e1",
    "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": "e7252845",
+   "id": "d9e9ffdd",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1723,7 +1723,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c063036e",
+   "id": "9ef66d2e",
    "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": "a87a9053",
+   "id": "03b79718",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1764,7 +1764,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f97ccc38",
+   "id": "cd217c38",
    "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": "ac816d47",
+   "id": "5efc184f",
    "metadata": {},
    "source": [
     "### 例外（exception）\n",
@@ -1783,7 +1783,7 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "id": "b1a8e696",
+   "id": "6dc886e5",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1816,7 +1816,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c07df643",
+   "id": "781562e4",
    "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": "059a1e23",
+   "id": "008f8606",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1855,7 +1855,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8b343d5e",
+   "id": "a8c899e4",
    "metadata": {},
    "source": [
     "## 実用編\n",
@@ -1870,7 +1870,7 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "id": "06d79141",
+   "id": "6236b293",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1930,7 +1930,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59c1257e",
+   "id": "b4c0ad98",
    "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": "5158fef5",
+   "id": "a0933473",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1991,7 +1991,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a7c141f0",
+   "id": "994dfb52",
    "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": "ece73c75",
+   "id": "88fc5bf7",
    "metadata": {},
    "outputs": [
     {
@@ -2027,7 +2027,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bf06ce84",
+   "id": "a47d29af",
    "metadata": {},
    "source": [
     "### NumPy\n",
@@ -2040,7 +2040,7 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "id": "6eff63db",
+   "id": "c7745b45",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2053,7 +2053,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2975d6e9",
+   "id": "03631b30",
    "metadata": {},
    "source": [
     "#### 配列を作る\n",
@@ -2066,7 +2066,7 @@
   {
    "cell_type": "code",
    "execution_count": 53,
-   "id": "17ac7980",
+   "id": "7ae5c1dc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2097,7 +2097,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aacda645",
+   "id": "1508fff9",
    "metadata": {},
    "source": [
     "#### 配列の要素を参照する"
@@ -2106,7 +2106,7 @@
   {
    "cell_type": "code",
    "execution_count": 54,
-   "id": "be60885d",
+   "id": "67526e97",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2144,7 +2144,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c78cdd5d",
+   "id": "a9b9737e",
    "metadata": {},
    "source": [
     "#### 配列を操作する"
@@ -2153,7 +2153,7 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "id": "e6e8fbeb",
+   "id": "8e1dd048",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2187,7 +2187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e1324e48",
+   "id": "52c89534",
    "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": "78827f0f",
+   "id": "f7989100",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2229,7 +2229,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "80f1790e",
+   "id": "a3ba14fd",
    "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": "54005efa",
+   "id": "6a0a1063",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2266,7 +2266,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98db7806",
+   "id": "474f597c",
    "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": "8243accf",
+   "id": "cfbc0191",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2328,7 +2328,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61d47646",
+   "id": "f0a31f44",
    "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": "a1ecc684",
+   "id": "8eaeb293",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2365,7 +2365,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b41ea110",
+   "id": "6959f939",
    "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": "420b70ae",
+   "id": "8c400caa",
    "metadata": {},
    "source": [
     "### Matplotlib\n",
@@ -2386,7 +2386,7 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "id": "18f015a3",
+   "id": "de2d15c2",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2400,7 +2400,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "973614ff",
+   "id": "54f074c3",
    "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": "092adaea",
+   "id": "16ae7ee7",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2451,7 +2451,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d631e0fd",
+   "id": "9eda1fc1",
    "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": "3bb847b9",
+   "id": "8d0c95cf",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2470,7 +2470,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f22efecab00>]"
+       "[<matplotlib.lines.Line2D at 0x7fb783ee99f0>]"
       ]
      },
      "execution_count": 62,
@@ -2499,7 +2499,7 @@
   {
    "cell_type": "code",
    "execution_count": 63,
-   "id": "34b75a26",
+   "id": "e822cbb8",
    "metadata": {
     "tags": [
      "remove-output"
@@ -2509,7 +2509,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f22eff26c80>"
+       "<matplotlib.legend.Legend at 0x7fb783f35330>"
       ]
      },
      "execution_count": 63,
@@ -2538,7 +2538,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e9cf8a04",
+   "id": "a7afb1a9",
    "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": "71b4a611",
+   "id": "9166f269",
    "metadata": {},
    "source": [
     "### Jupyter\n",
@@ -2559,14 +2559,14 @@
   {
    "cell_type": "code",
    "execution_count": 64,
-   "id": "1a978822",
+   "id": "1b7f64ca",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Using matplotlib backend: <object object at 0x7f22f12aedd0>\n"
+      "Using matplotlib backend: <object object at 0x7fb785266dd0>\n"
      ]
     }
    ],
@@ -2588,7 +2588,7 @@
   {
    "cell_type": "code",
    "execution_count": 65,
-   "id": "2b3bf8bc",
+   "id": "47bcff21",
    "metadata": {},
    "outputs": [
     {
@@ -2610,7 +2610,7 @@
   {
    "cell_type": "code",
    "execution_count": 66,
-   "id": "c969c677",
+   "id": "25c95be9",
    "metadata": {},
    "outputs": [
     {
diff --git a/_sources/qkc_machine_learning.ipynb b/_sources/qkc_machine_learning.ipynb
index f2f7001..a5d5898 100644
--- a/_sources/qkc_machine_learning.ipynb
+++ b/_sources/qkc_machine_learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "0dac9eb5",
+   "id": "883adea9",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d4e6e0aa",
+   "id": "c31c1885",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7dfe4fbd",
+   "id": "3db20b4a",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "41d9911e",
+   "id": "95245cb3",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39c44181",
+   "id": "7e3a8856",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d3c88a36",
+   "id": "eab608b2",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -202,7 +202,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1eed1e51",
+   "id": "6860b3bb",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "155a1fd5",
+   "id": "d7c014ec",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -274,7 +274,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "e4739139",
+   "id": "98821c8e",
    "metadata": {},
    "outputs": [
     {
@@ -306,7 +306,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "354586fb",
+   "id": "73df008f",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3416d6c2",
+   "id": "e1b84265",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -382,7 +382,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "e33ddf35",
+   "id": "c1d4bc2c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -410,7 +410,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "59e28000",
+   "id": "3ed2c258",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -453,7 +453,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "a6374c6c",
+   "id": "465b720b",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -479,7 +479,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c7852b68",
+   "id": "fbcfc1ac",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -492,7 +492,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "15331159",
+   "id": "105f20be",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -533,7 +533,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2569fe9a",
+   "id": "79334afc",
    "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": "598f2d03",
+   "id": "143e2247",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -583,7 +583,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f7856de",
+   "id": "867adc82",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -600,7 +600,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "b969cadc",
+   "id": "7a9f1878",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14bb94b0",
+   "id": "9d7368b5",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -663,7 +663,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "ef9f3ffc",
+   "id": "f86a6a29",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -702,7 +702,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "429dd937",
+   "id": "f5e60633",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/quantum_computation.ipynb b/_sources/quantum_computation.ipynb
index 0438ea9..242bffc 100644
--- a/_sources/quantum_computation.ipynb
+++ b/_sources/quantum_computation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "68e736de",
+   "id": "2344dc12",
    "metadata": {},
    "source": [
     "# 【課題】アダマールテスト\n",
@@ -20,7 +20,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "a09ccd8f",
+   "id": "bbd3534d",
    "metadata": {
     "tags": [
      "remove-output"
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "04b6e211",
+   "id": "748428e8",
    "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": "ee755754",
+   "id": "eed1da31",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3d8403ec",
+   "id": "4ebb2350",
    "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": "698f3f50",
+   "id": "1eb11917",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2955546",
+   "id": "7683b58d",
    "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": "8e368811",
+   "id": "bda71e6d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -200,7 +200,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "368a5ed6",
+   "id": "daea920c",
    "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": "4f01f8cc",
+   "id": "ab854882",
    "metadata": {
     "tags": [
      "remove-output"
@@ -273,7 +273,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "65fce16a",
+   "id": "af208bba",
    "metadata": {
     "tags": [
      "remove-output"
@@ -300,7 +300,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "325fd7ef",
+   "id": "949d3d1a",
    "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": "90f45340",
+   "id": "5e937d08",
    "metadata": {
     "tags": [
      "remove-output"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b06de4ad",
+   "id": "1258eec8",
    "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": "52959dd5",
+   "id": "c4e767c8",
    "metadata": {},
    "outputs": [
     {
@@ -417,7 +417,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "68c2d0fc",
+   "id": "4432c5e1",
    "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": "b3e4be1b",
+   "id": "b1819c42",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -440,7 +440,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b5eb98f6",
+   "id": "4f3acab1",
    "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": "4964b5b1",
+   "id": "4d6134dc",
    "metadata": {
     "tags": [
      "remove-output"
@@ -484,7 +484,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "1d995211",
+   "id": "c6d99256",
    "metadata": {
     "tags": [
      "remove-output"
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eed07207",
+   "id": "1e1229dd",
    "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": "5cdc268b",
+   "id": "712a45d9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -528,7 +528,7 @@
    "outputs": [
     {
      "data": {
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",
       "text/plain": [
        "<Figure size 1600x400 with 1 Axes>"
       ]
@@ -548,7 +548,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "57abe072",
+   "id": "c9dd276e",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/_sources/shor.ipynb b/_sources/shor.ipynb
index 415c907..116fa9a 100644
--- a/_sources/shor.ipynb
+++ b/_sources/shor.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "028ba9ee",
+   "id": "227b5731",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8979359",
+   "id": "999b20ae",
    "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": "5a651aaf",
+   "id": "ee6e48bc",
    "metadata": {},
    "source": [
     "(qpe)=\n",
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "947a7ad3",
+   "id": "8648a376",
    "metadata": {},
    "source": [
     "(qpe_1qubit)=\n",
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9ad5e056",
+   "id": "82005356",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "14a66724",
+   "id": "5b07b676",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -155,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "0870fd72",
+   "id": "fe6b76b1",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eaa5852a",
+   "id": "7cc33612",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -194,7 +194,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "d277cfef",
+   "id": "0051956a",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -228,7 +228,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6710dd8",
+   "id": "6dc9754a",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -243,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "2e6fabd7",
+   "id": "e80156f9",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "beb423ac",
+   "id": "ef86d1b2",
    "metadata": {},
    "source": [
     "**解答**\n",
@@ -332,7 +332,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "dd47870b",
+   "id": "0fb1d958",
    "metadata": {
     "tags": [
      "remove-input",
@@ -343,7 +343,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f56697b4df0>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7fb352a67c10>"
       ]
      },
      "execution_count": 4,
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c269fbf8",
+   "id": "337a9fda",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -406,7 +406,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "432125f3",
+   "id": "4a1d449f",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -455,7 +455,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8d4073b6",
+   "id": "b82e5870",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "92b5020f",
+   "id": "68638995",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -488,7 +488,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "91544945",
+   "id": "d7f21205",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -545,7 +545,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "4be66c9f",
+   "id": "1fec6b86",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -578,7 +578,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "904c16f8",
+   "id": "ef041502",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d99c2ce4",
+   "id": "cc168719",
    "metadata": {},
    "source": [
     "(shor_algo)=\n",
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d5b4e32d",
+   "id": "21dfed32",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ade920",
+   "id": "22019860",
    "metadata": {},
    "source": [
     "(shor_circuit)=\n",
@@ -733,7 +733,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "165d1bec",
+   "id": "1b58fed3",
    "metadata": {},
    "source": [
     "(shor_measurement)=\n",
@@ -793,7 +793,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32613325",
+   "id": "9afbec9f",
    "metadata": {},
    "source": [
     "(continued_fractions)=\n",
@@ -805,7 +805,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d2c05a1",
+   "id": "88d84140",
    "metadata": {},
    "source": [
     "(modular_exponentiation)=\n",
@@ -843,7 +843,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c9a64e36",
+   "id": "a6c1dde4",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -863,7 +863,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "4ddcb303",
+   "id": "57cacb86",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -909,7 +909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "21eb09db",
+   "id": "1e3fd794",
    "metadata": {},
    "source": [
     "(shor_imp_oracle)=\n",
@@ -927,7 +927,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "f7058b5c",
+   "id": "4678554c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -974,7 +974,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1d9cd3a",
+   "id": "d58f6fe9",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1106,7 +1106,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "53a9d202",
+   "id": "3f8186e5",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1151,7 +1151,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "412c3ef3",
+   "id": "1645163d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1167,7 +1167,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "66542ce4",
+   "id": "b5a5fe5c",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1225,7 +1225,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f264e7fb",
+   "id": "275b8ffc",
    "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": "813c348d",
+   "id": "a4d34895",
    "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": "ee8b007a",
+   "id": "f74e0ef5",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1280,7 +1280,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "50df3602",
+   "id": "72dc795d",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1296,10 +1296,10 @@
      "text": [
       "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",
-      " 64       64/256 = 0.250\n"
+      "128      128/256 = 0.500\n"
      ]
     }
    ],
@@ -1322,7 +1322,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6ed93af3",
+   "id": "b3582a96",
    "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": "4fdb36c0",
+   "id": "6fc0bbb6",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1347,10 +1347,10 @@
      "text": [
       "     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",
-      "     0.250       1/ 4             4\n"
+      "     0.500       1/ 2             2\n"
      ]
     }
    ],
@@ -1370,7 +1370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72cc8d60",
+   "id": "62b96e55",
    "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 a235c7b..b70ef68 100644
--- a/_sources/spectrum_estimation.ipynb
+++ b/_sources/spectrum_estimation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "b68f989e",
+   "id": "e891beac",
    "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": "9e9b7b4a",
+   "id": "d2ab4d1e",
    "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": "becef7fe",
+   "id": "10d6fc70",
    "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": "54344a15",
+   "id": "bfd60e96",
    "metadata": {
     "tags": [
      "remove-output"
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4200c5e0",
+   "id": "576b0c8d",
    "metadata": {},
    "outputs": [
     {
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2174457",
+   "id": "8da37634",
    "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": "d012a1a4",
+   "id": "9595d480",
    "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": "f372b635",
+   "id": "fc110877",
    "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": "36e6a95e",
+   "id": "467ae98a",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -353,7 +353,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "894e4981",
+   "id": "197455be",
    "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": "23ac8f58",
+   "id": "dbe9e1ce",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28ac47c0",
+   "id": "6f97dfe3",
    "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": "7fb7e360",
+   "id": "92116ecb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -453,7 +453,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af50a37c",
+   "id": "475bb094",
    "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": "326083e1",
+   "id": "f354a4d6",
    "metadata": {
     "tags": [
      "remove-output"
@@ -489,7 +489,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "9fc9e21f",
+   "id": "7c71151f",
    "metadata": {
     "tags": [
      "remove-output"
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "52485197",
+   "id": "de11f83e",
    "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": "9b9a444f",
+   "id": "291e5a89",
    "metadata": {
     "tags": [
      "remove-output"
@@ -574,7 +574,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "39ea2639",
+   "id": "f0add5d3",
    "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": "c825ca64",
+   "id": "02f83cc4",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2d874d34",
+   "id": "395fde49",
    "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": "4495fb57",
+   "id": "158e8258",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5fb6f6fb",
+   "id": "fbaeac95",
    "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": "c8f6623e",
+   "id": "49bbb3dd",
    "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": "7b618fec",
+   "id": "3f2c4efe",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -828,7 +828,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e352d888",
+   "id": "e5e94206",
    "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": "7955c24f",
+   "id": "c1bb78b1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -916,7 +916,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2fc73357",
+   "id": "0d2211bb",
    "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。"
@@ -925,7 +925,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "52693dbd",
+   "id": "5f7cdce1",
    "metadata": {
     "tags": [
      "remove-output"
@@ -961,7 +961,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0aadacdd",
+   "id": "00052616",
    "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。"
@@ -970,7 +970,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "6a02e656",
+   "id": "7312bf22",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1150,7 +1150,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "2fa7042f",
+   "id": "51c5fd8c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -1160,22 +1160,22 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f61acf58e80>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf589d0>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61ad604970>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58550>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5a950>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58970>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5a020>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58b50>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58b20>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5a170>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf581c0>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58fa0>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf58b80>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5aa10>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5ab00>,\n",
-       " <matplotlib.lines.Line2D at 0x7f61acf5abf0>]"
+       "[<matplotlib.lines.Line2D at 0x7fbe546f4d30>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f41f0>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f5210>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4910>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4ac0>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4760>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4970>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f6140>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe548b3040>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f5e10>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4a90>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f4a60>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f59c0>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f42b0>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f5d50>,\n",
+       " <matplotlib.lines.Line2D at 0x7fbe546f5b70>]"
       ]
      },
      "execution_count": 15,
@@ -1212,7 +1212,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "147e0fef",
+   "id": "7f9a61ef",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/_sources/toffoli_shor_code.ipynb b/_sources/toffoli_shor_code.ipynb
index 30fc1ae..0ceae87 100644
--- a/_sources/toffoli_shor_code.ipynb
+++ b/_sources/toffoli_shor_code.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "e73ef0ee",
+   "id": "606134a8",
    "metadata": {},
    "source": [
     "# ToffoliゲートとShorコード\n",
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "098fffa7",
+   "id": "74a1232f",
    "metadata": {},
    "source": [
     "## 量子エラー訂正\n",
@@ -49,7 +49,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5cea49a0",
+   "id": "808147ad",
    "metadata": {},
    "source": [
     "## Toffoliゲート\n",
@@ -60,7 +60,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "850830fd",
+   "id": "03265c9e",
    "metadata": {
     "tags": [
      "remove-input"
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fd958440",
+   "id": "2df9fa67",
    "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": "675f68fd",
+   "id": "dc80871c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -117,7 +117,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "fcc17f6d",
+   "id": "bc4beb4a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,7 +140,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bb13219",
+   "id": "1a359808",
    "metadata": {},
    "source": [
     "## CPゲート\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7aadb44a",
+   "id": "4d63d059",
    "metadata": {
     "tags": [
      "remove-output"
@@ -223,7 +223,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "38cbc49d",
+   "id": "ad624c07",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd580aad",
+   "id": "a82c82f0",
    "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": "8c7641ef",
+   "id": "49ffe0f7",
    "metadata": {},
    "source": [
     "## CCZゲート\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "ef8fafb4",
+   "id": "3949887c",
    "metadata": {
     "tags": [
      "remove-output"
@@ -320,7 +320,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "51a6e2f4",
+   "id": "6414350b",
    "metadata": {
     "tags": [
      "remove-output"
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1aeaf9df",
+   "id": "707c4707",
    "metadata": {},
    "source": [
     "## Hadamardゲート\n",
@@ -369,7 +369,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "3d8212c7",
+   "id": "e9e9b8eb",
    "metadata": {
     "tags": [
      "remove-output"
@@ -407,7 +407,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "366c0179",
+   "id": "a4bbaf21",
    "metadata": {
     "tags": [
      "remove-output"
@@ -434,7 +434,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64670db4",
+   "id": "ffb90e44",
    "metadata": {},
    "source": [
     "## Toffoliゲート\n",
@@ -445,7 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "ef3ec25a",
+   "id": "104818ca",
    "metadata": {
     "tags": [
      "remove-output"
@@ -485,7 +485,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "547b0808",
+   "id": "b45e07a2",
    "metadata": {
     "tags": [
      "remove-output"
@@ -512,7 +512,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "933f6182",
+   "id": "cad0770d",
    "metadata": {},
    "source": [
     "## 量子エラーとエラー訂正\n",
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6db042bc",
+   "id": "a300a424",
    "metadata": {},
    "source": [
     "### ビット反転（$X$）エラー\n",
@@ -544,7 +544,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "2edb0c1e",
+   "id": "9986f424",
    "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": "06442c61",
+   "id": "be2a9ed1",
    "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": "abf057d1",
+   "id": "e00c6fe3",
    "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": "f1594baf",
+   "id": "91f7efef",
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "024757e3",
+   "id": "0729ad35",
    "metadata": {},
    "source": [
     "### 位相反転（$Z$）エラー\n",
@@ -678,7 +678,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "9eb82756",
+   "id": "666d1054",
    "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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NHg0ACPC3Imr4RFypKuryGJ/mH8CY4XdixJBYAMC8u5/BoVM7utzuo8/ew+Sx30Fw/64nUvzzqUzM+sYy9A2wIcCvD+YkPYFDJ7s+hmxCQkKwfPlyhISEiC5FGI4Byc5sPTBiMhCW0P31P/2Na365ruajuyEwGEh82HUlsLfqxaWRL5RXX0Jw/2Gw/N9En4qiYMigEbhafetH1iuv2XH4zE58M+6hLo9xtboYQwf9/R25QwdFovJaGZxOxy23+0POdsyZsqIbZwGUVxdjyD8co6tzkFFNTQ0OHjyImprOZ083O44Byc5sPaAoQOxsIHyi5/fdLwSYtAgI6OVvwmSYI7fVN17Dj99NxqL70jA2YrJXjnH2q8NoaKrFlNgHvbJ/WZWUlOD5559vM/WObDgGJDsz9oCiAGO/DcR9Bx57y09YAjD5MdcbH3o7hjnJ3TEwos1VMl3XcbWqGEMGdjwrYkNjLf5l2xzcHT8fC6av6dYxhgwcgStVF1t/f6WqqM3VwI4c+PS/cf+kx2FRLd08jxG4+g/H6OwciIjIfBQFCJsATF0OhIzu+X76DgDuXADE3Q/49fFYeV7FMCe5Qf2GYEzYN/DHE66bBw6f3YWQgeEICxnTbt3rTXX4521zMHnsHKTMernd8idei0VFTWm7z5PGzkFB6QkUX3W9P3Dv//4X7ktc0mlN9Y3XcPjsTnwn6Yk2n+cXf4rUrd/ucJvpiQvxxxO/xvXmejQ7mnAwZzvum9j5MYiIyJys/YGJjwBTv++ai667V+pCRru2u3uFa9oTI5FyahJq60ePbsXrmcuxI/vnCLT2R+qid1uXbfzdk5g6bh7ujp+H3Uf+A19c+hSNzfU4cnY3AODexIVI+fZLqKq7imsNXyMosP1z24HWIDy/cBv+LeNhODUHIkPHI23xe63Ln35jIv59xX6EDBgOAPjzqQ8QHT4J4XdEt9nPlaoi9OlktsbEqPswPXExVm6cAAC4L3Ex7hrX9f18RERkTrbBrq9eo+8D6r8Grl0B6sqB0jOA5nC9zSFqGtA/FAgaYpyrcB1hmCNEDBmLTT/8W4fL1i7c1vq/U779ElK+/VKH65258Bd8957nOg1bd8e7AmFHtq451eb3c+9aibl3rWy33ukLf8GSGS92uA8AWDb7X7Fs9r92upwAq9WKuLg4WK1W0aUIwzEg2cnWA6rFFdaChrh+f/U80FQH+FuBkUlia/MUhjnyiOmJC71+jNWPbPH6McwuKioKu3fvFl2GUBwDkh17wHx4zxwRERGRgTHMEUkkNzcXCQkJyM3NFV2KMBwDkh17wHwY5ogkous6WlpaoOu66FKE4RiQ7NgD5sMwR0RERGRgfABCMqo/MGO16CrEUf1FV0DUPYGqBVUzZ4suwy2B3Zzkm4g8i2FOMoriuVedEJH3KIoC2y3ekkJEdAP/pCCSSFRUFPbu3YuIiAjRpQjDMSDZsQfMh2GOSCJWqxXR0dFdr2hiHAOSHXvAfPgABJFESktL8fLLL6O0tP07dGXBMSDZsQfMh2GOSCLV1dXYtWsXqqurRZciDMeAZMceMB+GOSIiIiIDY5gjIiIiMjCGOSIiIiIDY5gjkkhISAieeuophISEiC5FGI4ByY49YD4Mc0QSURQFAQEBUBRFdCnCcAxIduwB82GYI5JIeXk5tmzZgvLyctGlCMMxINmxB8yHYY6IiIjIwBjmiIiIiAyMYY6IiIjIwBjmiCQyYMAAJCcnY8CAAaJLEYZjQLJjD5iPouu6LroIIuq5/Px8rx8jNjbW68foKdnPn4g94J7DvwSa6oA+/YBv/ZPoajyDV+aIJNLU1ISLFy+iqalJdCnCcAxIduwB82GYI5JIQUEB5syZg4KCAtGlCMMxINmxB8zHT3QBRETUnq7raNCcostwS6Bq4US0RAIwzBER9UINmhODsj8WXYZbqmbOhs3Cv1aIfI1fsxIREREZGMMcERERkYHxejiRROLj45GXlye6DKE4BiQ79oD58MocERERkYExzBFJpLCwEEuWLEFhYaHoUoThGJDs2APmwzBHJJGGhgacPn0aDQ0NoksRhmNAsmMPmA/DHBEREZGBMcwRERERGRjDHBEREZGBMcwRSSQsLAzp6ekICwsTXYowHAOSHXvAfDjPHJFEBg4ciHnz5okuQyiOAcmOPWA+vDJHJJHKykq8//77qKysFF2KMBwDkh17wHwY5ogkUlZWhvXr16OsrEx0KcJwDEh27AHzkSLMVVRUIC0tDWPGjIHVakVERASee+451NfXY8WKFVAUBZs3bxZdJhEREXlRQxXgdLj+t+YEdE1sPZ5i+nvmTp06hQceeAB2ux02mw3jxo3D5cuXsWnTJly4cKH1MvPEiRPFFkpE5AXO7RnQPvgtLGt+BHXO/W2W6boOZ+qL0PPy4Ld5E5RRkWKKJPIiXQeungdKTgFVl/7+ect14Og2IDwRCEsE/K3CSrxtpr4yV1FRgeTkZNjtdqxduxZlZWU4ceIE7HY70tPTkZWVhZycHCiKgoSEBNHlEhF5nLosBYgcCefWd6CXV7RZpu3eA/3MWajLljLIkSlpTuDcAeDsh22D3A2N14CCw0DO+0BDtc/L8xhTh7nVq1ejpKQEzz77LDZs2ICgoKDWZWlpaUhMTITD4UBkZCT69+8vsFIi37DZbJg2bRpsNpvoUoSRbQwUf3/4pa4FGhvhfOOt1s/1SyXQMn4FJXYs1IWPiiuQfE6WHtB1IP9jwJ7b9boNVcDJnUCzQd9wZtowl5eXh8zMTISEhOCVV17pcJ1JkyYBABITE1s/uxH+pkyZgj59+kBRFJ/US+QLkZGR2LZtGyIjI0WXIoyMY6BEj4G6ZBH0z05AyzoA3emE87UNgK7DkroWisUiukTyIVl6oOYycPnz7q9/vRooOua1crzKtGFux44d0DQNKSkp6NevX4fr9O3bF0DbMFdQUIBdu3YhNDQUSUlJPqmVyFecTifq6urgdDpFlyKMrGOgpjwGjB4N5zvboG35JfQvzkNd/j0oEeGiSyMfk6UHSk65v83lc4CzxeOleJ1pw1x2djYAYMaMGZ2uU1JSAqBtmLv33ntRVlaGvXv3YtasWd4tksjH8vPzkZSUhPz8fNGlCCPrGCh+fvBLXQM0t0DblwVlfDzURx4WXRYJIEMPOJuBK+fd387RCJRf8Hw93mbap1kvXrwIABg5cmSHyx0OB44ePQqgbZhTVc/n28mTJ8Nut3t8v0QAsHz58m6ve2Neqf379+PkyZPd3i4jI8PNqnzHnfMHejYGIs5fDwgAtm7x7E5tNsDfH3A4oCRNhuLhP+9iomOgNDd7dJ/UNbP2wO0I6R+BDU/8rUfbvpz2Mxw8sdXDFXUtNDQUx48f79G2pg1z9fX1AIDr1693uDwzMxMVFRUICgrCqFGjvFqL3W5HaWmpV49B8mpo6P4du42Nja0/3dmuN//36855AD0bAyHnb+0Dfw/uTtd1ODe+CThagBER0P7nA6jT74UyfJjHjnG57DLQ2OSx/VH3mLYHboOjoefxpqam2nDna9owFxoaiqqqKpw4cQJTp05ts6ysrAypqakAgISEBK8/5BAaGurV/ZPcAgMDu72u1Wpt/enOdr35hdzunAfQszEQcf56QADKPbg/bc9e6KfPQP3+41Cn3gXHqh/CufFNWDake+zPwOHDhvPKnABm7YHbEeDnjxZHE/z9+ri9rUOtF3K+t5MVTBvmZs2ahby8PKSnp2P27NmIiYkBAOTk5GDZsmWoqHDNt+SLyYJ7etmUqDvcue/l3Llz2L59Ox588EHEx8d3e7v169f3pDSfcPe+n56MgYjzr3c6MCj7Y4/sSy8thbY9A8rYGKiLFkCxWKAuTYH27nvQ9uyF5bvzPXKc81+eh81i2r9Wei2z9sDt+nx/96YluZm/Fdj50S9h8eRlcR8w7QMQaWlpGDx4MC5duoT4+HhMmDAB0dHRmDJlCkaPHo2ZM2cCaHu/HJHZxcTE4OjRo63/uJGRbGOgaxqcr78BaBosqWtapyFRFy2AEhMNbXsG9Mt8R6dMZOmBiInubzN8PAwX5AATh7nw8HAcPnwYc+fOhdVqRVFREYKDg7F161ZkZWXh/HnXYy4McyQTf39/BAcHw9/fgH9aeYhsY6Dt3A09Nw/q40uhjBjR+rliscDywhpAc8K58U3oui6wSvIlWXqg/zAgzI2XOwUOAkZ+03v1eJNpwxwAxMXFYd++faitrUVtbS2OHTuGlStXor6+HkVFRVBVFePHjxddJpHPFBcX45lnnkFxcbHoUoSRaQz04mJo7/0aSlws1EcfabdciRwJdWkK9LOfQ9uzV0CFJIIsPaAowNhZwLBu/DUfGAzcuQAI6Ov9urxBypsbzp07B13XERMT0+HNnzt37gQA5Obmtvl9ZGQkJk+e7LtCiTystrYWhw4dwqpVq0SXIoxMY6CMGAH/rN/fch3LY4theWyxjyqi3kCmHlBVYNx3gDvGuCYRrixqu7zvQCB8IhA2AejBsxK9hpRh7uzZswA6/4p14cKFHf7+8ccfN9xcO0RERDJTFGDIGNev69XAp78BWhoB/77A3Stcy42OYa4DvHeEiIjIfPoOBNT/Sz6qxRxBDjD5PXOd6SrMERERERmFlFfmbry3lUg2Q4cOxbp16zB06FDRpQjDMSDZsQfMR8owRySrkJAQt9/jaDYcA5Ide8B8pPyalUhWNTU1OHjwIGpqakSXIgzHgGTHHjAfhjkiiZSUlOD5559HSUmJ6FKE4RiQ7NgD5sMwR0RERGRgDHNEREREBsYwR0RERGRgDHNEErFarYiLi4PVahVdijAcA5Ide8B8ODUJkUSioqKwe/du0WUIxTEg2bEHzIdX5oiIiIgMjGGOSCK5ublISEhAbm6u6FKE4RiQ7NgD5sMwRyQRXdfR0tICXddFlyIMx4Bkxx4wH94zR0TUCwWqFlTNnC26DLcEqhbRJRBJiWGOiKgXUhQFNgv/iCairvFrViIiIiID4z/7iCQSFRWFvXv3IiIiQnQpwnAMSHbsAfNhmCOSiNVqRXR0tOgyhOIYkOzYA+bDr1mJJFJaWoqXX34ZpaWloksRhmNAsmMPmA/DHJFEqqursWvXLlRXV4suRRiOAcmOPWA+DHNEREREBsYwR0RERGRgDHNEREREBsYwRyQRVVWRlJQEVZW39TkGJDv2gPnw/0kiiWiahpycHGiaJroUYTgGJDv2gPkwzBEREREZGMMcERERkYExzBEREREZGMMckUQGDBiA5ORkDBgwQHQpwnAMSHbsAfNRdF3XRRdBRD2Xn5/v9WPExsZ6/Rg9Jfv5E7EH3HP4l0BTHdCnH/CtfxJdjWfwyhyRRJqamnDx4kU0NTWJLkUYjgHJjj1gPgxzRBIpKCjAnDlzUFBQILoUYTgGJDv2gPn4iS6AiIja03UdDZpTdBluCVQtUBTFI/uS/fyJ3MEwR0TUCzVoTgzK/lh0GW6pmjkbNotn/lqR/fyJ3MGvWYmIiIgMjGGOiIiIyMB4PZhIIvHx8cjLyxNdhlAcA5Ide8B8eGWOiIiIyMAY5ogkUlhYiCVLlqCwsFB0KcJwDEh27AHzYZgjkkhDQwNOnz6NhoYG0aUIwzEg2bEHzIdhjoiIiMjAGOaIiIiIDIxhjoiIiMjAGOaIJBIWFob09HSEhYWJLkUYjgHJjj1gPpxnjkgiAwcOxLx580SXIRTHgGTHHjAfXpkjkkhlZSXef/99VFZWii5FGI4ByY49YD4Mc0QSKSsrw/r161FWVia6FGE4BiQ79oD5MMwRERERGZgUYa6iogJpaWkYM2YMrFYrIiIi8Nxzz6G+vh4rVqyAoijYvHmz6DKJiDzOuT0DLfc/CO3gR+2W6boOxwvr0DJ3HvTCIt8X5yMt9z/YrV/a6TOiSyUvcTqAsnPA8R1AU73rs+Z64MJRoKlObG2eYPoHIE6dOoUHHngAdrsdNpsN48aNw+XLl7Fp0yZcuHCh9Z6BiRMnii2UiMgL1GUp0D45BufWd6BM+gaUO0Jal2m790A/cxbqE8uhjIoUV6SXWdJe6HSZbrdD+9VvgAH9oYSH+7Aq8pWvi4DPs4CW620/13Wg8G9A0SfAiCRgzLcARRFS4m0zdZirqKhAcnIy7HY71q5di5/85CcICgoCALz22mtYt24d/Pz8oCgKEhISBFdL5H02mw3Tpk2DzWYTXYowso2B4u8Pv9S1cKx+Hs433oLfK+sBAPqlEmgZv4ISOxbqwkcFV+ld6qyZHX6uNzbC+aO1gKrC8i8vQhkc7OPKxJCpB8ovAKf3ANA7X0fXgYufAo5GIHa2MQOdqb9mXb16NUpKSvDss89iw4YNrUEOANLS0pCYmAiHw4HIyEj0799fYKVEvhEZGYlt27YhMjJSdCnCyDgGSvQYqEsWQf/sBLSsA9CdTjhf2wDoOiypa6FYLKJLFMK58S3gq0KoTz4B9c6JosvxGVl6oKkeOPshbhnkblZ6xvVVrBGZNszl5eUhMzMTISEheOWVVzpcZ9KkSQCAxMTE1s927tyJRx99FCNHjkRgYCBiY2Px0ksvoa7OBF+qk/ScTifq6urgdDpFlyKMrGOgpjwGjB4N5zvboG35JfQvzkNd/j0oEXJ+tej87U7of/krlOn3wrLgEdHl+JQsPVB6BtAc7m1TfMJ1pc5oTBvmduzYAU3TkJKSgn79+nW4Tt++fQG0DXMbNmyAxWLBz3/+cxw4cAA/+MEP8Itf/AJz5syBpmk+qZ3IW/Lz85GUlIT8/HzRpQgj6xgofn7wS10DNLdA25cFZXw81EceFl2WENqJk9C2ZwCjImFZ8yPR5ficDD2ga64w5666q0CNAWdsMe09c9nZ2QCAGTNmdLpOSUkJgLZh7sMPP8Qdd9zR+vvp06fjjjvuQEpKCo4cOYJ7773X7VomT54Mu93u9nZE3bF8+fJur3tjXqn9+/fj5MmT3d4uIyPDzap8x53zB3o2BiLOXw8IALZu8exObTbA3x9wOKAkTYaievbf8zHRMVCamz2yL6+cPwDdfgXOn6cDgYHw+8mPofS1emzfnjx/d5i1B27HQNtQvPXUZz3a9kdP/Rh/PP2uhyvqWmhoKI4fP96jbU0b5i5evAgAGDlyZIfLHQ4Hjh49CqBtmLs5yN0wefJkAEBpaWmParHb7T3elqgrDQ0N3V63sbGx9ac72/Xm/37dOQ+gZ2Mg5PytfeDvwd3pug7nxjcBRwswIgLa/3wAdfq9UIYP89gxLpddBhqbPLMzD58/4HrgwfHTnwF1dbD8v5949NwBD5+/G0zbA7dBH9zzkN54vcVw52vaMFdf75pI5vr16x0uz8zMREVFBYKCgjBq1Khb7uvQoUMAgLi4uB7VEhoa2qPtiLojMDCw2+tardbWn+5s15tfyO3OeQA9GwMR568HBKDcg/vT9uyFfvoM1O8/DnXqXXCs+iGcG9+EZUM6FA89vjd82HCPXpnz5PkDgPPNTcCFr6A+vgzqlCQP792z5+8Os/bA7Qjq696Y3My/j5jzvZ2soOi6EW/169q4ceOQl5eHzZs3Y9WqVW2WlZWVYdKkSSgrK8O0adNw5MiRTvdTWlqKO++8E5MmTcKBAwe8XTaR29y57+XcuXNYsGABdu7cifj4+G5vFxsb25PSfMLd+356MgYizr/e6cCg7I89si+9tBSOf3oWSuRIWN7aCMVigXNHJrR334P6g6dh+e58jxynauZs2CyeuUbgyfMHAOfO3dDe3gZl6l2w/NuPPRZgb+bJ83eHWXvgdn36a+DaFfe3m/Yk0Hegx8vxKtM+ADFr1iwAQHp6Os6fP9/6eU5ODmbMmIGKigoAt54suK6uDvPnz0dAQAC2b9/u1XqJfCEmJgZHjx5FTEyM6FKEkW0MdE2D8/U3AE2DJXVN6zQk6qIFUGKioW3PgH7ZgHd8u0E7dRratu1AeDgsaS94JcgZiSw9ED7R/W1CRhsvyAEmDnNpaWkYPHgwLl26hPj4eEyYMAHR0dGYMmUKRo8ejZkzXZNI3ny/3M2uX7+O5ORkFBYW4qOPPsKwYZ69t4JIBH9/fwQHB8Pf39N3IxmHbGOg7dwNPTcP6uNLoYwY0fq5YrHA8sIaQHPCufFNmPRLGuhfV8L5768Amgb1W9Og/+0TaH/M7vCX/lWh6HJ9QpYeGBrrZjBTgMgp3qrGu0wb5sLDw3H48GHMnTsXVqsVRUVFCA4OxtatW5GVldV6ta6jMNfS0oIFCxbg+PHjOHDgAMaNG+fr8om8ori4GM888wyKi4tFlyKMTGOgFxdDe+/XUOJioT7afi41JXIk1KUp0M9+Dm3PXgEVep9eUgLUXAMAaDsy4XxtQ6e/tCNHBVfrG7L0gMUfuPNRoE/Hs5O1pQDj5gADDTrtomkfgABcDyzs27ev3ed1dXUoKiqCqqoYP358m2U35qb705/+hP3792PKFIPGdKIO1NbW4tChQ+3uI5WJTGOgjBgB/6zf33Idy2OLYXlssY8q8j01MQHqR/tFl9GryNQDgYOApBTgiz+5Xu3V0dsgbIOB6Omur1iNytRhrjPnzp2DruuIiYlp9yTPqlWr8Lvf/Q4vvvgiAgMD8cknn7Qui4qK6nDqEiIiIuqdrEFA4sNA4zWg9CzQUOWaVDgg0PVV7MAwY76P9WZShrmzZ88C6Pgr1htPrL766qt49dVX2yx799133Z6ckYiIiMSz9geipomuwjsY5v5BUVGRj6shIiIi6jnTPgBxK7cKc0RmNnToUKxbtw5Dhw4VXYowHAOSHXvAfKS8Mnfjva1EsgkJCZH+VgGOAcmOPWA+Ul6ZI5JVTU0NDh48iJqaGtGlCMMxINmxB8yHYY5IIiUlJXj++edRUlIiuhRhOAYkO/aA+TDMERERERkYwxwRERGRgTHMERERERkYwxyRRKxWK+Li4mC1WkWXIgzHgGTHHjAfKacmIZJVVFQUdu/eLboMoTgGJDv2gPnwyhwRERGRgTHMEUkkNzcXCQkJyM3NFV2KMBwDkh17wHwY5ogkous6WlpaoOu66FKE4RiQ7NgD5sN75oiIeqFA1YKqmbNFl+GWQNXi0X3JfP5E7mCYIyLqhRRFgc0i7x/Rsp8/kTv4NSsRERGRgfGfPUQSiYqKwt69exERESG6FGE4BiQ79oD5MMwRScRqtSI6Olp0GUJxDEh27AHz4desRBIpLS3Fyy+/jNLSUtGlCMMxINmxB8yHYY5IItXV1di1axeqq6tFlyIMx4Bkxx4wH4Y5IiIiIgNjmCMiIiIyMIY5IiIiIgNjmCOSSEhICJ566imEhISILkUYjgHJjj1gPorOl7MRERERGRavzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZGMMcERERkYExzBEREREZ2P8H1RLPqNuW2cwAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 789.163x284.278 with 1 Axes>"
       ]
@@ -741,7 +741,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "ef3e5ad0",
+   "id": "90de5e63",
    "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": "f8db0dfb",
+   "id": "74041609",
    "metadata": {},
    "source": [
     "### Shorコード\n",
@@ -776,7 +776,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "54d5af46",
+   "id": "a1fa82a9",
    "metadata": {
     "tags": [
      "remove-output"
@@ -785,9 +785,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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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": "bc8ed479",
+   "id": "bc02f12e",
    "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 8c8d1c6..208b02a 100644
--- a/_sources/vqc_machine_learning.ipynb
+++ b/_sources/vqc_machine_learning.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "1d9136ad",
+   "id": "1c83156d",
    "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": "66ef9d77",
+   "id": "63edd28d",
    "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": "f6b5a0d0",
+   "id": "ec695581",
    "metadata": {},
    "source": [
     "## はじめに <a id='introduction'></a>\n",
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4ee03b41",
+   "id": "f1fbbd40",
    "metadata": {},
    "source": [
     "## 機械学習と深層学習 <a id='ml'></a>\n",
@@ -89,7 +89,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d11b22e",
+   "id": "8ba74b49",
    "metadata": {},
    "source": [
     "## 量子回路学習<a id='qml'></a>\n",
@@ -119,7 +119,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "50fafe0c",
+   "id": "8beca2d1",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9a72ec96",
+   "id": "ab9e59f9",
    "metadata": {},
    "source": [
     "## 初歩的な例<a id='example'></a>\n",
@@ -174,7 +174,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "3923d966",
+   "id": "2c78da96",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7535d786",
+   "id": "6fee9473",
    "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": "43fdedea",
+   "id": "448e0e60",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0187a524",
+   "id": "a4881aa3",
    "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": "55face5b",
+   "id": "185a8a84",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1d64f2a7",
+   "id": "01cd6aaf",
    "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": "7d900cc8",
+   "id": "5905d2c2",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -421,7 +421,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "c46dfb2e",
+   "id": "95c611d6",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7b7a6b37",
+   "id": "b22603de",
    "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": "8696d205",
+   "id": "7186c798",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -495,7 +495,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "caa412ad",
+   "id": "3bd92bcf",
    "metadata": {
     "tags": [
      "remove-input"
@@ -512,7 +512,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "03227c1e",
+   "id": "82647af8",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -548,7 +548,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "99dee4f9",
+   "id": "8eeecc9d",
    "metadata": {
     "tags": [
      "remove-input"
@@ -567,7 +567,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "930da886",
+   "id": "f75d2c78",
    "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": "7041319d",
+   "id": "512114d9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f9f5c0b8640>]"
+       "[<matplotlib.lines.Line2D at 0x7f9578208cd0>]"
       ]
      },
      "execution_count": 11,
@@ -606,7 +606,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e45318f4",
+   "id": "b370f2c1",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -622,7 +622,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "c3ae669d",
+   "id": "d26f7705",
    "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": "342b86b0",
+   "id": "22bdf3da",
    "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": "2938dfc3",
+   "id": "93cc398b",
    "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": "94448924",
+   "id": "22ca314e",
    "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": "2dab386c",
+   "id": "53bcff31",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -765,7 +765,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bd793fcb",
+   "id": "02ec058d",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -806,7 +806,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "84ea5ff5",
+   "id": "55c038ef",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -836,7 +836,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6752c001",
+   "id": "c4333b2b",
    "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": "0d6cd854",
+   "id": "60549636",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -883,7 +883,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "69a26c46",
+   "id": "b92b5b00",
    "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": "8e3f1840",
+   "id": "8092362d",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -956,7 +956,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "b61eddbe",
+   "id": "c2b2f3ba",
    "metadata": {
     "tags": [
      "remove-input"
@@ -972,7 +972,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "9cee90b9",
+   "id": "f5ba935f",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1026,7 +1026,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "14cfd832",
+   "id": "902e4ebe",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1069,7 +1069,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "306ad155",
+   "id": "c99e2d19",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1098,7 +1098,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f5160c0a",
+   "id": "a57707d8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/vqe.ipynb b/_sources/vqe.ipynb
index 35e33a5..01004fd 100644
--- a/_sources/vqe.ipynb
+++ b/_sources/vqe.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "5106cd42",
+   "id": "6c1fcc4c",
    "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": "0e970cfb",
+   "id": "63278116",
    "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": "6867d6d6",
+   "id": "605d62e6",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a969a7ac",
+   "id": "3ef8521b",
    "metadata": {},
    "source": [
     "## 量子力学における変分法\n",
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "360bcd11",
+   "id": "1104d5ea",
    "metadata": {},
    "source": [
     "(vqa)=\n",
@@ -102,7 +102,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "175feb10",
+   "id": "ccf9f35d",
    "metadata": {},
    "source": [
     "### 変分量子回路\n",
@@ -115,7 +115,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b01c021d",
+   "id": "53204344",
    "metadata": {},
    "source": [
     "### 単純な変分フォーム\n",
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "295740ed",
+   "id": "36b5743d",
    "metadata": {},
    "source": [
     "### パラメータの最適化\n",
@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "99f93c9b",
+   "id": "8d26bbe9",
    "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": "8337d306",
+   "id": "ffc6b1e7",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -194,7 +194,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0480d513",
+   "id": "fed1892f",
    "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": "bd167eaa",
+   "id": "4aefafef",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "38412489",
+   "id": "c507002b",
    "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": "32466ad4",
+   "id": "e32d0574",
    "metadata": {
     "tags": [
      "remove-output"
@@ -263,7 +263,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f753b2b6740>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7fed285e6710>"
       ]
      },
      "execution_count": 3,
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "19da40f0",
+   "id": "85e35760",
    "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": "1419c8d0",
+   "id": "d293760e",
    "metadata": {},
    "outputs": [
     {
@@ -313,7 +313,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "16377759",
+   "id": "e117dfca",
    "metadata": {},
    "outputs": [
     {
@@ -335,7 +335,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4975be39",
+   "id": "5f040683",
    "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": "cdc69493",
+   "id": "3bd77dc2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -368,7 +368,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fe7a9216",
+   "id": "e07f39e2",
    "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": "d380dbc8",
+   "id": "d307733c",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<X> = 0.7498\n",
-      "<Y> = 0.431\n",
-      "<Z> = 0.4992\n"
+      "<X> = 0.7326\n",
+      "<Y> = 0.4362\n",
+      "<Z> = 0.5154\n"
      ]
     }
    ],
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4cb444ee",
+   "id": "68ee637c",
    "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": "a71a0797",
+   "id": "3a87604e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -444,7 +444,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f6a799af",
+   "id": "7eb53bf2",
    "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": "1135f8fb",
+   "id": "393b5b42",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -472,7 +472,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e59a8a83",
+   "id": "6cf691c9",
    "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": "9d0b118b",
+   "id": "f6be11db",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -499,7 +499,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "d3d1536c",
+   "id": "ccd90b33",
    "metadata": {
     "tags": [
      "remove-input"
@@ -517,7 +517,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "07ddfa03",
+   "id": "f2ab89a8",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -552,7 +552,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "bd357ed6",
+   "id": "2985bad8",
    "metadata": {
     "tags": [
      "remove-input"
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1f0d999e",
+   "id": "bbec6325",
    "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": "3a90f645",
+   "id": "68f32035",
    "metadata": {},
    "outputs": [
     {
@@ -599,7 +599,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "3d8b2795",
+   "id": "9ce619c4",
    "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。"
@@ -608,7 +608,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "52d7e7dd",
+   "id": "87226c25",
    "metadata": {},
    "outputs": [
     {
@@ -628,7 +628,7 @@
   },
   {
    "cell_type": "raw",
-   "id": "ba05da77",
+   "id": "8b30a095",
    "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": "07f5d92d",
+   "id": "5bac1736",
    "metadata": {},
    "source": [
     "#### Estimatorの利用\n",
@@ -647,13 +647,13 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "98a64972",
+   "id": "6fb8c2b9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.7408, 0.4436, 0.5082])"
+       "array([0.7594, 0.4466, 0.4988])"
       ]
      },
      "execution_count": 16,
@@ -679,7 +679,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2f2b2387",
+   "id": "83b3e5bc",
    "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": "cc3286cb",
+   "id": "2dbbfa2e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -709,7 +709,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a597e98b",
+   "id": "f8bd8170",
    "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": "0683cec2",
+   "id": "93296eae",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -736,7 +736,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "df46ccf7",
+   "id": "20c37483",
    "metadata": {
     "tags": [
      "remove-input"
@@ -753,7 +753,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "2d057613",
+   "id": "c30fd87c",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -788,7 +788,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "bcadd464",
+   "id": "ab82c6d5",
    "metadata": {
     "tags": [
      "remove-input"
@@ -806,7 +806,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "236aa59a",
+   "id": "92859a81",
    "metadata": {},
    "outputs": [
     {
@@ -826,7 +826,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4df5f1ff",
+   "id": "7b42c6d9",
    "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": "d1296b3d",
+   "id": "7e18049e",
    "metadata": {
     "tags": [
      "remove-output"
@@ -855,7 +855,7 @@
     {
      "data": {
       "text/plain": [
-       "<qiskit.circuit.instructionset.InstructionSet at 0x7f7534b8f190>"
+       "<qiskit.circuit.instructionset.InstructionSet at 0x7fed21ec7a90>"
       ]
      },
      "execution_count": 23,
@@ -875,7 +875,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "82cef06b",
+   "id": "d8f7d681",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -900,7 +900,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "3ffe0e6d",
+   "id": "a692c5c7",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -931,7 +931,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "07df65d0",
+   "id": "488ba3a2",
    "metadata": {
     "tags": [
      "remove-input"
@@ -948,7 +948,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "5417ddad",
+   "id": "88315191",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -983,7 +983,7 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "id": "4b50adc9",
+   "id": "c40f0265",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1001,7 +1001,7 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "08b7e3e9",
+   "id": "231a8d9e",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1028,7 +1028,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6aed100f",
+   "id": "c27b7f08",
    "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": "0f42aac7",
+   "id": "a9b0b08a",
    "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": "f3d4bb92",
+   "id": "e0c56b1c",
    "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": "c7911b38",
+   "id": "214c85f8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1147,7 +1147,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c8491531",
+   "id": "1fc1b571",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -1168,7 +1168,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "f551f067",
+   "id": "8aa776fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1180,7 +1180,7 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "id": "9ea8d2a2",
+   "id": "6934f065",
    "metadata": {},
    "outputs": [
     {
@@ -1227,7 +1227,7 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "id": "5456e33f",
+   "id": "c31db1af",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1259,7 +1259,7 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "id": "1dfe1b3b",
+   "id": "6b8d9242",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1276,7 +1276,7 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "id": "531b56f0",
+   "id": "912116d4",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -1306,7 +1306,7 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "id": "e53c1095",
+   "id": "1b217dab",
    "metadata": {
     "tags": [
      "remove-input"
@@ -1323,7 +1323,7 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "id": "753b4d9b",
+   "id": "380014f9",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -1355,7 +1355,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0192aa93",
+   "id": "af4b8bc8",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
diff --git a/_sources/vqe_tracking.ipynb b/_sources/vqe_tracking.ipynb
index a1336c7..4bfd229 100644
--- a/_sources/vqe_tracking.ipynb
+++ b/_sources/vqe_tracking.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "274bfe10",
+   "id": "258d494c",
    "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": "a1d8da97",
+   "id": "c0cdc701",
    "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": "e8bad58a",
+   "id": "3667560c",
    "metadata": {},
    "source": [
     "## はじめに\n",
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "26855579",
+   "id": "c48ad03f",
    "metadata": {},
    "source": [
     "(hep)=\n",
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84eb38c5",
+   "id": "e09ca2b7",
    "metadata": {},
    "source": [
     "(hep_detect)=\n",
@@ -77,7 +77,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c91b3022",
+   "id": "095067bc",
    "metadata": {},
    "source": [
     "(ML_challenge)=\n",
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6f06d7fc",
+   "id": "b462083d",
    "metadata": {},
    "source": [
     "(tracking)=\n",
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "93222212",
+   "id": "bbf11d28",
    "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": "3f70a955",
+   "id": "da516b39",
    "metadata": {
     "jupyter": {
      "outputs_hidden": false
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d9cbea8",
+   "id": "70988dda",
    "metadata": {},
    "source": [
     "(hamiltonian_form)=\n",
@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "98761509",
+   "id": "fab505b6",
    "metadata": {},
    "source": [
     "(pre_processing)=\n",
@@ -185,7 +185,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b4905a4",
+   "id": "d76230b0",
    "metadata": {},
    "source": [
     "#### QUBO\n",
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4b50c779",
+   "id": "4b1a1dea",
    "metadata": {},
    "outputs": [
     {
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2c84034e",
+   "id": "42b2159a",
    "metadata": {},
    "source": [
     "#### Ising形式\n",
@@ -262,7 +262,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "33032217",
+   "id": "a3ec94ec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "afda34b7",
+   "id": "8d0ec2eb",
    "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": "a4f267c4",
+   "id": "19bf0219",
    "metadata": {
     "tags": [
      "raises-exception",
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8f072c79",
+   "id": "0845182b",
    "metadata": {},
    "source": [
     "(tracking_vqe)=\n",
@@ -359,7 +359,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "bd4395c9",
+   "id": "22886c15",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -396,7 +396,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "12cf486a",
+   "id": "a4713afe",
    "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": "6e7480b4",
+   "id": "9bad1dc9",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -468,7 +468,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d830bfc",
+   "id": "d90af36f",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
@@ -486,7 +486,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "68f0ca0d",
+   "id": "d19f9c35",
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -546,7 +546,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b3a28585",
+   "id": "3fd851e9",
    "metadata": {},
    "source": [
     "**提出するもの**\n",
diff --git a/circuit_from_scratch.html b/circuit_from_scratch.html
index e039dae..bf3f7c7 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">
-\[\displaystyle | \text{final} \rangle =  - 0.556 |0:11\rangle + 0.831 |1:11\rangle\]</div>
+\[\displaystyle | \text{final} \rangle =  - 0.383 |0:10\rangle + 0.924 |1:10\rangle\]</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 0x7f1e3071b2e0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;qiskit.circuit.instructionset.InstructionSet at 0x7f97d80f0670&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>0.203244
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.203578
 </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>0.202827
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>0.202314
 </pre></div>
 </div>
 </div>
diff --git a/dynamics_simulation.html b/dynamics_simulation.html
index 800df34..8733090 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/cd0eb9636a5fca9451684c32d501ad431487d47aef5c32fa10af92f25f45cee8.png" src="_images/cd0eb9636a5fca9451684c32d501ad431487d47aef5c32fa10af92f25f45cee8.png" />
+<img alt="_images/9fdaeed84f00043d0b37b5dfa2298a6d0c07b0d5d78a28d42fb94758af5ec90e.png" src="_images/9fdaeed84f00043d0b37b5dfa2298a6d0c07b0d5d78a28d42fb94758af5ec90e.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 89fd438..a3fa94a 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/6adfed56fd48474a2dc73f66d82b04dff019e551b30d8718611c670980cb5a52.png" src="_images/6adfed56fd48474a2dc73f66d82b04dff019e551b30d8718611c670980cb5a52.png" />
+<img alt="_images/2e17d5d3e744c2096c2b2d29aafe09fc9628bed5f4ced4cf3ecc631b29e1cf39.png" src="_images/2e17d5d3e744c2096c2b2d29aafe09fc9628bed5f4ced4cf3ecc631b29e1cf39.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/207f71cccde3ff3eb3646cc1befd935edc12dcec0865e14e37d851038e77979e.png" src="_images/207f71cccde3ff3eb3646cc1befd935edc12dcec0865e14e37d851038e77979e.png" />
+<img alt="_images/591e5622b6f6bbabc38d005de17caf21d92faaa55eed308e92470a2a29edb494.png" src="_images/591e5622b6f6bbabc38d005de17caf21d92faaa55eed308e92470a2a29edb494.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/3f951bd0116295b42bb109c7ecb1e05fec560ec46d52850de6f7c50d8da75cc8.png" src="_images/3f951bd0116295b42bb109c7ecb1e05fec560ec46d52850de6f7c50d8da75cc8.png" />
+<img alt="_images/04632f8f8e6319aaca48bd976ba79775a3f619f1cccf36a101fca5031756ecf3.png" src="_images/04632f8f8e6319aaca48bd976ba79775a3f619f1cccf36a101fca5031756ecf3.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 1278daf..f411ca0 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 0x7f1842f71d80&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.legend.Legend at 0x7f36c838dd20&gt;
 </pre></div>
 </div>
 <img alt="_images/8fe7f6cec002f84c92a9a309fc997cb87d2f0c92efbc0bed59c6a03862da1888.png" src="_images/8fe7f6cec002f84c92a9a309fc997cb87d2f0c92efbc0bed59c6a03862da1888.png" />
diff --git a/python_intro.html b/python_intro.html
index b5839b9..3f10f2d 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 0x7f22f12aedd0&gt;
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Using matplotlib backend: &lt;object object at 0x7fb785266dd0&gt;
 </pre></div>
 </div>
 </div>
diff --git a/reports/extreme_simd.err.log b/reports/extreme_simd.err.log
index af629f5..76429ee 100644
--- a/reports/extreme_simd.err.log
+++ b/reports/extreme_simd.err.log
@@ -42,8 +42,8 @@ Transpiling the original circuit with standard settings
 ----- stdout -----
 Transpiling the optimized circuit with trivial mapping onto a chain of qubits
 ----- stdout -----
-Number of operations in the original circuit: 482
-  Breakdown: N(Rz)=199, N(X)=14, N(SX)=78, N(CNOT)=178
+Number of operations in the original circuit: 519
+  Breakdown: N(Rz)=226, N(X)=13, N(SX)=104, N(CNOT)=163
 Number of operations in the optimized circuit: 383
 ------------------
 
diff --git a/searchindex.js b/searchindex.js
index 6acd96c..e93869b 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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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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"\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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\ No newline at end of file
diff --git a/shor.html b/shor.html
index 2264fb3..2518457 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/509c759e690316305d0d671f2971e00cefd8b78c6f0f14f2994625543bf2c4b6.png" src="_images/509c759e690316305d0d671f2971e00cefd8b78c6f0f14f2994625543bf2c4b6.png" />
+<img alt="_images/8ab4bb083d809cee4fa406acee184a74b3fba3a3ae13c37f62170fb4d6f32462.png" src="_images/8ab4bb083d809cee4fa406acee184a74b3fba3a3ae13c37f62170fb4d6f32462.png" />
 </div>
 </div>
 </section>
@@ -1278,10 +1278,10 @@ <h1>素因数分解アルゴリズムを学習する<a class="headerlink" href="
 <div class="cell_output docutils container">
 <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
-  0        0/256 = 0.000
  64       64/256 = 0.250
+  0        0/256 = 0.000
+128      128/256 = 0.500
 </pre></div>
 </div>
 </div>
@@ -1306,10 +1306,10 @@ <h1>素因数分解アルゴリズムを学習する<a class="headerlink" href="
 <div class="cell_output docutils container">
 <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
-     0.000       0/ 1             1
      0.250       1/ 4             4
+     0.000       0/ 1             1
+     0.500       1/ 2             2
 </pre></div>
 </div>
 </div>
diff --git a/vqc_machine_learning.html b/vqc_machine_learning.html
index dbfef54..b95a2b0 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>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f9f5c0b8640&gt;]
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[&lt;matplotlib.lines.Line2D at 0x7f9578208cd0&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/4514f06982997e3792c4dccd295e30a5115abcf781cc7152e8e0551d65ce7229.png" src="_images/4514f06982997e3792c4dccd295e30a5115abcf781cc7152e8e0551d65ce7229.png" />
+<img alt="_images/bcfb89dbd7fb5f19e2597786fec3bb504a79f834c6d8f8f54d402e701e6f1ac6.png" src="_images/bcfb89dbd7fb5f19e2597786fec3bb504a79f834c6d8f8f54d402e701e6f1ac6.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 94411c3..ff2ab89 100644
--- a/vqe.html
+++ b/vqe.html
@@ -744,9 +744,9 @@ <h3><a class="toc-backref" href="#id27">変分フォームを使った実例</a>
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.7498
-&lt;Y&gt; = 0.431
-&lt;Z&gt; = 0.4992
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;X&gt; = 0.7326
+&lt;Y&gt; = 0.4362
+&lt;Z&gt; = 0.5154
 </pre></div>
 </div>
 </div>
@@ -871,7 +871,7 @@ <h4><a class="toc-backref" href="#id28">Estimatorの利用</a><a class="headerli
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.7408, 0.4436, 0.5082])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([0.7594, 0.4466, 0.4988])
 </pre></div>
 </div>
 </div>