From 686e6b1ab5b0b0f7fb60f6a819401dae92d85515 Mon Sep 17 00:00:00 2001
From: cognigami <53974528+cognigami@users.noreply.github.com>
Date: Wed, 13 Oct 2021 17:31:49 -0400
Subject: [PATCH] Removes sections related to experimental features. (#4573)

Based on recent work with qsim team.
---
 docs/noise.ipynb | 136 -----------------------------------------------
 1 file changed, 136 deletions(-)

diff --git a/docs/noise.ipynb b/docs/noise.ipynb
index 68423929ec5..8c13992d715 100644
--- a/docs/noise.ipynb
+++ b/docs/noise.ipynb
@@ -627,142 +627,6 @@
     "Note that the order of Kraus operators is reversed from above, but this of course does not affect the action of the channel."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "c0b3d1faa7ca"
-   },
-   "source": [
-    "## Noise Properties"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "740b0efc2bbc"
-   },
-   "source": [
-    "The `NoiseProperties` object describes the behavior of the noise to be added to a circuit. These properties include p00, p11, Pauli error, depolarization decay constant, T1 constant, and XEB fidelity. For more information on these metrics, see [this paper](https://arxiv.org/pdf/1910.11333.pdf)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d451328e34ab"
-   },
-   "outputs": [],
-   "source": [
-    "from cirq.devices.noise_properties import NoiseProperties, NoiseModelFromNoiseProperties\n",
-    "noise_prop = NoiseProperties(p00 = 0.2, pauli_error = 0.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "289d782f599a"
-   },
-   "source": [
-    "We can then use the `noise_model_from_noise_properties` method to turn a NoiseProperties object into a NoiseModel, that can then be use with a simulator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "91b211c9b61f"
-   },
-   "outputs": [],
-   "source": [
-    "noise_model = NoiseModelFromNoiseProperties(noise_prop)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "b9b8ddcbe207"
-   },
-   "source": [
-    "This `NoiseModel` object can be applied to a `Simulator` (described below)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "935e20561b7c"
-   },
-   "source": [
-    "## Converting calibration metrics to noise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "cf14dc6703db"
-   },
-   "source": [
-    "[Calibration metrics](quantumai.google/cirq/google/calibration) measure the behavior of Google's quantum computers. The `cirq_google.experiments.noise_models.calibration_to_noise_properties` turns a `Calibration` object into a `NoiseProperties` object, that can then be turned into a `NoiseModel`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "d8d51f3601d5"
-   },
-   "outputs": [],
-   "source": [
-    "import cirq_google\n",
-    "from google.protobuf.text_format import Merge\n",
-    "\n",
-    "t1 = 0.5 # microseconds\n",
-    "p00 = 0.1\n",
-    "\n",
-    "_CALIBRATION_DATA = Merge(\n",
-    "f\"\"\"\n",
-    "timestamp_ms: 1579214873,\n",
-    "metrics: [{{\n",
-    "    name: 'single_qubit_idle_t1_micros',\n",
-    "    targets: ['0_0'],\n",
-    "    values: [{{\n",
-    "        double_val: {t1}\n",
-    "    }}]\n",
-    "}}, {{\n",
-    "    name: 'single_qubit_p00_error',\n",
-    "    targets: ['0_0'],\n",
-    "    values: [{{\n",
-    "        double_val: {p00}\n",
-    "    }}]\n",
-    "}}]\n",
-    "\"\"\",\n",
-    "    cirq_google.api.v2.metrics_pb2.MetricsSnapshot(),\n",
-    ")\n",
-    "\n",
-    "calibration = cirq_google.Calibration(_CALIBRATION_DATA)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "da9d5f3e2c72"
-   },
-   "source": [
-    "This Calibration object can then be passed to the `compare_generated_noise_to_metrics` method in `cirq_google` to compare actual metrics with those generated from the noise model created to match the calibration data. Sample output from this method is shown below:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3d203521ff55"
-   },
-   "source": [
-    "```\n",
-    "   Metric  Initial value  Measured value\n",
-    "0    p00            0.1        0.104000\n",
-    "1     T1          500.0      575.445484\n",
-    "```"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {