{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:03.275691Z",
     "iopub.status.busy": "2025-05-30T09:12:03.275441Z",
     "iopub.status.idle": "2025-05-30T09:12:03.279687Z",
     "shell.execute_reply": "2025-05-30T09:12:03.279172Z"
    },
    "id": "60d1a836f542"
   },
   "outputs": [],
   "source": [
    "# Copyright 2021 The Cirq Developers\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "058c886e7d6d"
   },
   "source": [
    "# State Histograms\n",
    "State Histograms are useful to visualize the output of running a quantum circuit. For details on how to create and run your own quantum circuits, please see [Cirq basics](../start/basics.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "78cd9ffef102"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://quantumai.google/cirq/simulate/state_histograms\"><img src=\"https://quantumai.google/site-assets/images/buttons/quantumai_logo_1x.png\" />View on QuantumAI</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/quantumlib/Cirq/blob/main/docs/simulate/state_histograms.ipynb\"><img src=\"https://quantumai.google/site-assets/images/buttons/colab_logo_1x.png\" />Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/quantumlib/Cirq/blob/main/docs/simulate/state_histograms.ipynb\"><img src=\"https://quantumai.google/site-assets/images/buttons/github_logo_1x.png\" />View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/Cirq/docs/simulate/state_histograms.ipynb\"><img src=\"https://quantumai.google/site-assets/images/buttons/download_icon_1x.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:03.282244Z",
     "iopub.status.busy": "2025-05-30T09:12:03.282025Z",
     "iopub.status.idle": "2025-05-30T09:12:18.440087Z",
     "shell.execute_reply": "2025-05-30T09:12:18.439287Z"
    },
    "id": "614826b3777a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "installing cirq...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "tensorflow-metadata 1.17.1 requires protobuf<4.22,>=4.21.6; python_version < \"3.11\", but you have protobuf 5.29.5 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0m"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "installed cirq.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import cirq\n",
    "except ImportError:\n",
    "    print(\"installing cirq...\")\n",
    "    !pip install --quiet cirq\n",
    "    print(\"installed cirq.\")\n",
    "    import cirq\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "acebe6240f0b"
   },
   "source": [
    "### Basic usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:18.443698Z",
     "iopub.status.busy": "2025-05-30T09:12:18.443273Z",
     "iopub.status.idle": "2025-05-30T09:12:18.608661Z",
     "shell.execute_reply": "2025-05-30T09:12:18.607891Z"
    },
    "id": "c1c6adeeb0e1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = cirq.LineQubit.range(4)\n",
    "circuit = cirq.Circuit([cirq.H.on_each(*q), cirq.measure(*q)])\n",
    "result = cirq.Simulator().run(circuit, repetitions=100)\n",
    "_ = cirq.plot_state_histogram(result, plt.subplot())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "da358e505449"
   },
   "source": [
    "### Plotting circuits with sparse output\n",
    "If the results have sparse output, you can use the `result.histogram` to compute the histogram and plot only states that have non-zero sample count. For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3bed4f1d4222"
   },
   "source": [
    "#### Default plot\n",
    "The default call to `cirq.plot_state_histogram` would plot all possible output states (including 0's). This can get messy, especially if the output is sparse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:18.611423Z",
     "iopub.status.busy": "2025-05-30T09:12:18.611189Z",
     "iopub.status.idle": "2025-05-30T09:12:18.957402Z",
     "shell.execute_reply": "2025-05-30T09:12:18.956715Z"
    },
    "id": "789958187e7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "q = cirq.LineQubit.range(6)\n",
    "circuit = cirq.Circuit(\n",
    "    [\n",
    "        cirq.H(q[0]),\n",
    "        [cirq.CNOT(q[0], q[i]) for i in range(1, 6, 1)],\n",
    "        cirq.measure(*q, key='measure_all'),\n",
    "    ]\n",
    ")\n",
    "result = cirq.Simulator().run(circuit, repetitions=100)\n",
    "_ = cirq.plot_state_histogram(result, plt.subplot())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d7c05a17460b"
   },
   "source": [
    "#### Sparse plots\n",
    "To plot only non-zero entries in the histogram, you can first compute a histogram using `result.histogram()` and pass its result to `cirq.plot_state_histogram`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:18.960222Z",
     "iopub.status.busy": "2025-05-30T09:12:18.959975Z",
     "iopub.status.idle": "2025-05-30T09:12:19.052358Z",
     "shell.execute_reply": "2025-05-30T09:12:19.051758Z"
    },
    "id": "2405b58effd0"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "histogram = result.histogram(key='measure_all')\n",
    "_ = cirq.plot_state_histogram(histogram, plt.subplot())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7dab59bc0b62"
   },
   "source": [
    "### Histogram for processed results.\n",
    "`result.histogram()` also allows you to pass a `fold_func` parameter that can be used to process the sampled measurement results and convert to a countable value to generate a histogram.\n",
    "\n",
    "As an example, suppose you want to plot a histogram of the parity of the number of times a `1` occurs in a measurement result. We can do this using `fold_func` as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:19.055148Z",
     "iopub.status.busy": "2025-05-30T09:12:19.054888Z",
     "iopub.status.idle": "2025-05-30T09:12:19.148707Z",
     "shell.execute_reply": "2025-05-30T09:12:19.148067Z"
    },
    "id": "64d8a54596bc"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def count_ones(bits):\n",
    "    return 'even' if sum(bits) % 2 == 0 else 'odd'\n",
    "\n",
    "\n",
    "q = cirq.LineQubit.range(6)\n",
    "# Uniform superposition over all 2 ** 6 states.\n",
    "circuit = cirq.Circuit([cirq.H.on_each(*q), cirq.measure(*q, key='measure_all')])\n",
    "result = cirq.Simulator().run(circuit, repetitions=100)\n",
    "# Create a customized histogram by processing the measurement results using `fold_func`.\n",
    "custom_histogram = result.histogram(key='measure_all', fold_func=count_ones)\n",
    "# Plot the custom histogram using cirq.plot_state_histogram\n",
    "_ = cirq.plot_state_histogram(custom_histogram, plt.subplot())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ac5483d0a913"
   },
   "source": [
    "### Modifying plot properties\n",
    "You can pass additional arguments to the `cirq.plot_state_histogram` method to modify plot properties like `title`, `xlabel`, `ylabel` and `tick_label`. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-30T09:12:19.151625Z",
     "iopub.status.busy": "2025-05-30T09:12:19.151043Z",
     "iopub.status.idle": "2025-05-30T09:12:19.279961Z",
     "shell.execute_reply": "2025-05-30T09:12:19.279334Z"
    },
    "id": "16aa0365af6a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def binary_labels(num_qubits):\n",
    "    return [bin(x)[2:].zfill(num_qubits) for x in range(2**num_qubits)]\n",
    "\n",
    "\n",
    "q = cirq.LineQubit.range(3)\n",
    "circuit = cirq.Circuit([cirq.H.on_each(*q), cirq.measure(*q)])\n",
    "result = cirq.Simulator().run(circuit, repetitions=100)\n",
    "_ = cirq.plot_state_histogram(\n",
    "    result,\n",
    "    plt.subplot(),\n",
    "    title='Custom Plot Title',\n",
    "    xlabel='Custom X-Axis Label',\n",
    "    ylabel='Custom Y-Axis Label',\n",
    "    tick_label=binary_labels(3),\n",
    ")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "state_histograms.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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