{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Noise\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Options\n",
    "1. rotation error\n",
    "2. tsp error\n",
    "3. decay\n",
    "4. decoheremce\n",
    "5. thermal factor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Incorporating Noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import *\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noises can be added in the options which are passed to the backend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Circuit\n",
    "q = QuantumRegister(3)\n",
    "c = ClassicalRegister(3)\n",
    "qc = QuantumCircuit(q,c)\n",
    "qc.u1(3.6,0)\n",
    "qc.cx(0,1)\n",
    "qc.u1(2.6,2)\n",
    "qc.cx(1,0)\n",
    "qc.s(2)\n",
    "qc.y(2)\n",
    "qc.measure(q,c,basis='Ensemble',add_param='Z')\n",
    "backend = BasicAer.get_backend('dm_simulator')\n",
    "# Noise parameters\n",
    "options = {}\n",
    "options1 = {\n",
    "    \"thermal_factor\": 0.,\n",
    "    \"decoherence_factor\": .9,\n",
    "    \"depolarization_factor\": 0.99,\n",
    "    \"bell_depolarization_factor\": 0.99,\n",
    "    \"decay_factor\": 0.99,\n",
    "    \"rotation_error\": {'rx':[1., 0.], 'ry':[1., 0.], 'rz': [1., 0.]},\n",
    "    \"tsp_model_error\": [1., 0.],\n",
    "    \"plot\": False\n",
    "}\n",
    "# Execution with and without noise\n",
    "run = execute(qc,backend,**options)\n",
    "result = run.result()\n",
    "run_error = execute(qc,backend,**options1)\n",
    "result_error = run_error.result()\n",
    "# Final state (probabilities)\n",
    "prob = result['results'][0]['data']['ensemble_probability']\n",
    "prob1 = result_error['results'][0]['data']['ensemble_probability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "labels = prob1.keys()\n",
    "without_noise = prob.values()\n",
    "with_noise = prob1.values()\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, without_noise, width, label='Without Noise')\n",
    "rects2 = ax.bar(x + width/2, with_noise, width, label='With Noise')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Probability')\n",
    "ax.set_title('Ensemble Probabilities with Noise')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}