Stabilizer Code IBM/Stabilizer Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stabilizer Code on IBM Quantum Computers\n",
    "\n",
    "Use [4,2,2] stabilizer code to extend coherence times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Razander/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/ibmqfactory.py:181: UserWarning: Credentials are already in use. The existing account in the session will be replaced.\n",
      "  warnings.warn('Credentials are already in use. The existing '\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from tqdm.notebook import trange, tqdm\n",
    "\n",
    "from qiskit import *\n",
    "IBMQ.load_account()\n",
    "\n",
    "import pickle\n",
    "import random\n",
    "from itertools import permutations\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Realizable Group R(2)\n",
    "helpful [implementation](https://github.com/Octophi/quantum_rb/blob/master/Find%20R(2)%20.ipynb)\n",
    "\n",
    "\n",
    "There are 576 elements in this group. The generates are $X_1$, $X_2$, $Z_1$, $Z_2$, $CNOT_{1,2}$, $CNOT_{2,1}$, $SWAP_{1,2} (H_1 H_2)$, $(Z_1 Z_2) CZ_{1,2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Single qubit gates in single qubit Hilbert space\n",
    "x = np.array([[0,1],[1,0]])\n",
    "iden = np.identity(2)\n",
    "z = np.array([[1,0],[0,-1]])\n",
    "h = np.array([[1, 1], [1, -1]]) / np.sqrt(2)\n",
    "\n",
    "# Logic gates for two qubits in [4,2,2] code. Generators of R(2) group.\n",
    "X1 = np.kron(iden, x)\n",
    "X2 = np.kron(x, iden)\n",
    "Z1 = np.kron(iden, z)\n",
    "Z2 = np.kron(z, iden)\n",
    "CNOT12 = np.array([[1,0,0,0],[0,1,0,0],[0,0,0,1],[0,0,1,0]])\n",
    "CNOT21 = np.array([[1,0,0,0],[0,0,0,1],[0,0,1,0],[0,1,0,0]])\n",
    "SWAP12_H1H2 = np.matmul(np.array([[1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]]), np.kron(h, h))\n",
    "Z1Z2_CZ12 = np.matmul(np.kron(z, z), np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,-1]]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R(2) group element generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize R(2) group\n",
    "r2_group = {}\n",
    "r2_group['0'] = np.identity(4)\n",
    "r2_group['1'] = X1\n",
    "r2_group['2'] = X2\n",
    "r2_group['3'] = Z1\n",
    "r2_group['4'] = Z2\n",
    "r2_group['5'] = SWAP12_H1H2\n",
    "r2_group['6'] = Z1Z2_CZ12\n",
    "r2_group['7'] = CNOT12\n",
    "r2_group['8'] = CNOT21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop: 1\n",
      "new keys: 36\n",
      "loop: 2\n",
      "new keys: 101\n",
      "loop: 3\n",
      "new keys: 157\n",
      "loop: 4\n",
      "new keys: 146\n",
      "loop: 5\n",
      "new keys: 88\n",
      "loop: 6\n",
      "new keys: 33\n",
      "loop: 7\n",
      "new keys: 6\n",
      "loop: 8\n",
      "new keys: 0\n"
     ]
    }
   ],
   "source": [
    "# temporary storage of R(2) group elements. Helpful in generating R(2) group elements from generators\n",
    "r2_group_previous = {} \n",
    "r2_group_previous['1'] = X1\n",
    "r2_group_previous['2'] = X2\n",
    "r2_group_previous['3'] = Z1\n",
    "r2_group_previous['4'] = Z2\n",
    "r2_group_previous['5'] = SWAP12_H1H2\n",
    "r2_group_previous['6'] = Z1Z2_CZ12\n",
    "r2_group_previous['7'] = CNOT12\n",
    "r2_group_previous['8'] = CNOT21\n",
    "\n",
    "for n in range(1, 9):\n",
    "    r2_group_new = {}  # temporary storage of R(2) group elements\n",
    "    for key_str, gate in r2_group_previous.items():\n",
    "        for i in range(1, 9):\n",
    "            if str(i) == key_str[-1]:  # don't consider sqaure of a gate\n",
    "                continue\n",
    "            new_key = key_str + str(i)\n",
    "            # make new gate based on new_key\n",
    "            new_gate = np.identity(4)\n",
    "            for j in new_key:\n",
    "                new_gate = np.matmul(new_gate, r2_group[j])\n",
    "            new_gate = np.around(new_gate, decimals = 2)\n",
    "            # Check if matrix has already been added to group\n",
    "            unique = True\n",
    "            for gate in r2_group_new.values():\n",
    "                rounded_new_gate = np.around(new_gate, decimals = 2)\n",
    "                rounded_gate = np.around(gate, decimals = 2)\n",
    "                if np.array_equal(rounded_gate, rounded_new_gate) or np.array_equal(rounded_gate, -1*rounded_new_gate):\n",
    "                    unique = False\n",
    "                    break\n",
    "            for gate in r2_group.values():\n",
    "                rounded_new_gate = np.around(new_gate, decimals = 2)\n",
    "                rounded_gate = np.around(gate, decimals = 2)\n",
    "                if np.array_equal(rounded_gate, rounded_new_gate) or np.array_equal(rounded_gate, -1*rounded_new_gate):\n",
    "                    unique = False\n",
    "                    break\n",
    "            # If new_gate has not been added to R(2) group, add it to R(2) group\n",
    "            if unique:\n",
    "                r2_group_new[new_key] = new_gate\n",
    "    r2_group.update(r2_group_new)  # update r2_group with new gates\n",
    "    r2_group_previous = r2_group_new  # new gates will be considered \"previous\" on next iteration\n",
    "    print('loop:', n)\n",
    "    print('new R(2) group elements:', len(list(r2_group_new.keys())))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "576"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(r2_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save R(2) group for later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save R(2) group dictionary r2_group\n",
    "# filename = 'r2_group.pkl'\n",
    "# outfile = open(filename, 'wb')\n",
    "# pickle.dump(r2_group, outfile)\n",
    "# outfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get R(2) group elements from r2_group.pkl file\n",
    "filename = 'r2_group.pkl'\n",
    "infile = open(filename,'rb')\n",
    "r2_group = pickle.load(infile)\n",
    "infile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "576"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(r2_group)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make random selection of R(2) gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate random selection of gates from R(2). Return concatenated string indicies for selection.\n",
    "def randR2Gates(n):\n",
    "    random.seed(a=2)\n",
    "    rand_gate_ind = ''\n",
    "    for _ in range(n):\n",
    "        rand_gate_ind = ''.join([rand_gate_ind, random.choice(list(r2_group.keys()))])\n",
    "    return rand_gate_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unencoded Randomized Benchmarking with R(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_state(state, qubit_map, qc, qr):\n",
    "    qc.barrier()\n",
    "    if state == '01':\n",
    "        qc.x(qr[qubit_map[0]])\n",
    "    elif state == '10':\n",
    "        qc.x(qr[qubit_map[1]])\n",
    "    elif state == '11':\n",
    "        qc.x(qr[qubit_map[0]])\n",
    "        qc.x(qr[qubit_map[1]])\n",
    "    qc.barrier()\n",
    "\n",
    "# Assume only 2 qubits\n",
    "def swap(qubit_map):\n",
    "    new_qubit_map = {}\n",
    "    new_qubit_map[0] = qubit_map[1]\n",
    "    new_qubit_map[1] = qubit_map[0]\n",
    "    return new_qubit_map\n",
    "\n",
    "def addGate(i, qubit_map, qr, qc, inverse=False):\n",
    "    if i == '1':\n",
    "        qc.x(qr[qubit_map[0]])        \n",
    "    elif i == '2':\n",
    "        qc.x(qr[qubit_map[1]])\n",
    "    elif i == '3':\n",
    "        qc.z(qr[qubit_map[0]])\n",
    "    elif i == '4':\n",
    "        qc.z(qr[qubit_map[1]])\n",
    "    elif i == '5':\n",
    "        if inverse:\n",
    "            qubit_map = swap(qubit_map)\n",
    "            qc.h(qr[qubit_map[0]])\n",
    "            qc.h(qr[qubit_map[1]])\n",
    "        else:\n",
    "            qc.h(qr[qubit_map[0]])\n",
    "            qc.h(qr[qubit_map[1]])\n",
    "            qubit_map = swap(qubit_map)\n",
    "    elif i == '6':\n",
    "        if inverse:\n",
    "            qc.z(qr[qubit_map[0]])\n",
    "            qc.z(qr[qubit_map[1]])\n",
    "            qc.cz(qr[qubit_map[0]], qr[qubit_map[1]])\n",
    "        else:\n",
    "            qc.cz(qr[qubit_map[0]], qr[qubit_map[1]])\n",
    "            qc.z(qr[qubit_map[0]])\n",
    "            qc.z(qr[qubit_map[1]])\n",
    "    elif i == '7':\n",
    "        qc.cx(qr[qubit_map[0]], qr[qubit_map[1]])\n",
    "    elif i == '8':\n",
    "        qc.cx(qr[qubit_map[1]], qr[qubit_map[0]])\n",
    "    else:\n",
    "        print(' --- Error ---\\nExceeded gate range')\n",
    "    qc.barrier()\n",
    "\n",
    "# Add random set of gates selected by randR2Gates. Includes gate inverses as well\n",
    "def addRandGates(rand_gate_indices, qubit_map, qr, qc):\n",
    "    # Add gates based on random index\n",
    "    for gate_indices in rand_gate_indices:\n",
    "        for gate in gate_indices:\n",
    "            addGate(gate, qubit_map, qr, qc)\n",
    "    # Add inverse of gates\n",
    "    for gate_indices in rand_gate_indices[::-1]:\n",
    "        for gate in gate_indices:\n",
    "            addGate(gate, qubit_map, qr, qc, inverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unencoded_rb(half_circuit_length, initial_state, shots, backend):\n",
    "    n_qubits = 2\n",
    "    qubit_map = {}  # used to track virtual swaps\n",
    "    for i in range(n_qubits):\n",
    "        qubit_map[i] = i\n",
    "\n",
    "    # Initialize quantum circuit and registers\n",
    "    qr = QuantumRegister(n_qubits)\n",
    "    cr = ClassicalRegister(n_qubits)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    \n",
    "    # Initialize quantum state\n",
    "    init_state(initial_state, qubit_map, qc, qr)\n",
    "    # Add randomized gates (along with inverses)\n",
    "    rand_gate_indices = randR2Gates(half_circuit_length)\n",
    "    addRandGates(rand_gate_indices, qubit_map, qr, qc)\n",
    "    # Measure qubits\n",
    "    for i in range(n_qubits):\n",
    "        qc.measure(i, i)\n",
    "\n",
    "    # Execute quantum circuit\n",
    "    result = execute(qc, backend, shots=shots).result()\n",
    "    return result, qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find least busy 5 qubit quantum computer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BackendStatus(backend_name='ibmq_burlington', backend_version='1.1.4', operational=True, pending_jobs=2, status_msg='active')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter backends to find the least busy 5 qubit quantum computer and check current \"pending_jobs\"\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "smaller_qcs = provider.backends(filters=lambda x: x.configuration().n_qubits == 5 and not x.configuration().simulator)\n",
    "backend_real = least_busy(smaller_qcs)\n",
    "\n",
    "# Check status and \"pending_jobs\" to see how long it might take to run\n",
    "backend_real.status()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start up simulator backend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "backend_sim = BasicAer.get_backend('qasm_simulator')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DlSSphDbdC3eHiHgjMK7ytj4zf99+JUmSVP3aFKARMRi4Hngf8Oe/TI57gI9m5updLixJ0n6kraNwfwCMASYDr618HQ8cgs8DlSR1I209hPtO4B2ZOavJtEcj4uPAA+1XliRJ1a2te6CrgJYepr0J8PCtJKnbaGuAXglcFRFv2DGh8vpblXmSJHULZW4mfwiwJCKeq7x/A/AS8F8ozpFKkrTf82bykiSV4M3kJUkqwZvJS5JUQpsCNCJ6RcQVEfG7iHgpIrY3/eqoIiVJqjZt3QP9KnAuxajbPwMXA9dQXMLyifYtTZKk6tXWAP0QcEFm/jOwHbgrMz8DXA6c0t7FSZJUrdoaoEOBRZXXLwIHVl7/P+BvWrOCiPhERDxbOQQ8OyIm76btiRGRLXy9uVm7D0bEoojYUvn+gTZulyRJbdLWAF0GDKu8fobi1n4AxwCb97RwRJwJXA1MBeqAx4D7ImLEHhYdD9Q2+Xq6yTqPAW4DfggcUfl+R0T8Ves2SZKktmtrgP4b8I7K66uBKyLiWeBGWncThYuAGzNzRmbWZ+angUbgwj0s98fMfL7JV9MBS58DHszMr1fW+XXgV5XpkiR1iDbdTD4zL2ny+scRsRw4FvhdZt6zu2UjohcwCfhms1n3V9axO09ERG+Kw8dfy8wHm8w7Bvhus/Y/Bz61h3VKklRaqQdq75CZvwF+08rmrwd6ACubTV8JnLyLZXbsnf4W6AX8D+CXEXFCZv660uagXazzoFbWJUlSm7U5QCPirRSHRw+rTKoHvpOZc9qzMIDMbAAamkyaFRGjKC6f+XVLy+xJRJwPnA9QW1vLnDlF2cOGDaNv374888wzAAwcOJDRo0czd+5cAHr06MHEiRNpaGhg48bigTTjxo1jzZo1wKAypVSlHb+P0aNHs3XrVpYvXw7A0KFDqampob6+HoB+/foxduxY5s2bx/btxRH1uro6Fi9ezLp16wAYM2YMmzZtYsWKFUDx+x4wYAANDUWX9u/fn0MPPZS5c+eSmUQEdXV1PP3002zYsAGAsWPHsn79ehobG4G966eVK4vPWcOHD6dXr14sXrwYgEGDBjFixAjmzZsHQM+ePTn88MOpr69n8+bi1P5hhx3GqlWrWLVqFQAjR44kIliyZAkAgwcPpra2lgULFgDQu3dvxo8fz8KFC9myZQsAEyZMoLGxkdWriwcXjRo1isxk6dKlAAwZMoQhQ4awaFExTq9Pnz6MGzeO+fPns23bNgAmTpzIsmXLWLt2rf1kP9lPHdRPrRWZuedWOxpHnA3cDMwEdjwT9Gjgr4HzMvPW3Szbi+KxZx/OzDuaTL8GmJCZJ7SyhsuBszJzXOX9MuC7mTm9SZuLgU9l5sjdrauuri5nzpzZmh+7W1Nu2n8CdNq5a7u6BEnqUjU1NbMz88g9tWvrIKKvA5dl5imZ+eXK198AlwFf292CmbkVmM2rrxc9hWI0bmsdQXFod4dZ7bBOSZLapK2HcIcAt7cw/Q6KEN2TbwO3RMTjwKPABRSXxVwHEBE3A2TmOZX3nwOWAAspzoF+BDgN+GCTdV4NPBwRXwL+HfgAcBLw9rZtmiRJrdfWAH0QOJHiGtCmTgQe2tPCmXlbRAwG/oHies4FwLszc2mlSfPrQXsB04HhFNeZLgTek5n3NlnnYxFxFsUe8JXA74EzM/M/2rRlkiS1QWseqH16k7f3Af8YEUfyl9G3RwOnA19pzQ/MzGuBa3cx78Rm76cB01qxzh/jc0slSZ2o7AO1XxnJ2sR32UUwSpK0v2nNA7V9ZqgkSc0YjpIkldDmAI2I90TEwxHxQkSsioiHIuLdHVGcJEnVqk0BGhEfo7ih/O+Bvwe+BDwL/FtEfLT9y5MkqTq19TKWvwcuysz/02Ta9RExmyJM/2+7VSZJUhVr6yHcERQPz27uPmC3t82TJGl/UuaB2s1vmwfwN8DSFqZLkrRfaush3G8C3608kWXHvWaPo3jM2KfbszBJkqpZWx+o/c8R8UfgCxR3H4LicWYfysy72rs4SZKqVasDNCIOoDhU+3Bm/lvHlSRJUvVr9TnQzHwZ+AnQv+PKkSRp39DWQUTzgDEdUYgkSfuStgboV4BvRcRpEXFwRNQ0/eqA+iRJqkptHYX7s8r3nwDZZHpU3vdoj6IkSap2bQ3QkzqkCkmS9jGtCtCI6AtMB04DegIPAJ/JzBc6sDZJkqpWa8+BXgGcR3EI918o7kb0vQ6qSZKkqtfaQ7inA/8zM/8VICJ+CDwaET0yc3uHVSdJUpVq7R7owcCvd7zJzMeBl4FhHVGUJEnVrrUB2gPY2mzay7R9EJIkSfuF1gZgALdGxJYm014LzIiITTsmZOb72rM4SZKqVWsD9KYWpt3anoVIkrQvaVWAZubfdnQhkiTtS9p6Kz9JkoQBKklSKQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCZ0eoBHxiYh4NiJeiojZETF5N21Pj4j7I2JVRGyIiP+IiPc1a3NeRGQLX6/t+K2RJHVXnRqgEXEmcDUwFagDHgPui4gRu1jkBGAm8J5K+3uBf2shdDcBtU2/MvOl9t8CSZIKB3Tyz7sIuDEzZ1Tefzoi3gVcCFzSvHFmfrbZpCsi4j3AacCvd26az3dEwZIktaTT9kAjohcwCbi/2az7gWPbsKr+wNpm0/pExNKIWB4R90RE3V6UKknSHnXmHujrgR7AymbTVwInt2YFEfFJYDhwS5PJDcBHgXkU4fpZ4NGImJiZT7ewjvOB8wFqa2uZM2cOAMOGDaNv374888wzAAwcOJDRo0czd+5cAHr06MHEiRNpaGhg48aNAIwbN441a9YAg1pT/j5hx+9j9OjRbN26leXLlwMwdOhQampqqK+vB6Bfv36MHTuWefPmsX37dgDq6upYvHgx69atA2DMmDFs2rSJFStWAMXve8CAATQ0NADQv39/Dj30UObOnUtmEhHU1dXx9NNPs2HDBgDGjh3L+vXraWxsBPaun1auLP7pDR8+nF69erF48WIABg0axIgRI5g3bx4APXv25PDDD6e+vp7NmzcDcNhhh7Fq1SpWrVoFwMiRI4kIlixZAsDgwYOpra1lwYIFAPTu3Zvx48ezcOFCtmzZAsCECRNobGxk9erVAIwaNYrMZOnSpQAMGTKEIUOGsGjRIgD69OnDuHHjmD9/Ptu2bQNg4sSJLFu2jLVr19pP9pP91EH91FqRma1uvDciYhjwHHBCZj7cZPqXgbMzc+welv8gRXCemZk/3U27HsCTwIOZ+ZndrbOuri5nzpzZhq1o2ZSb9p8AnXZu8517SepeampqZmfmkXtq15mDiF4AtgNDm00fCuz2/GVEnEERnufsLjwBMnM78ARwaPlSJUnavU4L0MzcCswGTmk26xSK0bgtiogPUYTneZn54z39nIgI4C1AY/lqJUnavc4ehftt4JaIeBx4FLgAGAZcBxARNwNk5jmV92dRhOcXgYcj4qDKerZm5ppKm8uB3wBPAwOAz1AE6IWdtE2SpG6oUwM0M2+LiMHAP1Bcr7kAeHdmLq00aX496AUUNV5V+drhIeDEyusDge8DBwHrgLnA8Zn5eEdsgyRJ0Pl7oGTmtcC1u5h34u7e72KZzwOfb4/aJElqLe+FK0lSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKCSJJVggEqSVIIBKklSCQaoJEklGKBSxQMPPMDb3vY2Jk2axFVXXfWq+Vu2bOGjH/0okyZN4uSTT2bZsmUAPPjgg5x00kkcd9xxnHTSSTz88MOvLPO1r32NCRMmcPDBB3faduxv7BdVKwNUArZv386UKVO4/fbbmTVrFnfeeSdPPfXUTm1uvfVWDjzwQGbPns2FF17IV77yFQAGDx7Mj370Ix599FGuueYaLrzwwleWeec738kDDzzQmZuyX7FfqlPZDzVr1qzhfe97HwcffDBTpkzZaZk777yT4447jre//e2cccYZrF69ulO2ZW8YoBIwe/ZsDjnkEEaNGkWvXr04/fTTue+++3Zqc++993LWWWcB8P73v5+HH36YzOQtb3kLtbW1AIwbN47NmzezZcsWAI466igOOuigzt2Y/Yj9Un325kNN7969ufTSS7nyyit3av/yyy9zySWXcPfdd/PII48wfvx4ZsyY0VmbVJoBKgGNjY284Q1veOX9sGHDaGxs3GWbAw44gAEDBrBmzZqd2tx9991MnDiR3r17d3zR3YD9Un325kNNv379OProo1/VD5lJZrJp0yYykw0bNuwTH3AO6OoCpP1FfX09V1xxBXfeeWdXl6Im7Jf21dKHmtmzZ++yTdMPNYMHD25xnT179uSb3/wmxx13HP369WP06NFMnz694zainbgHKgG1tbU899xzr7xfsWLFK4f/Wmrz8ssvs379empqagB47rnnOOecc7j22ms55JBDOq/w/Zz90j1s27aNG264gYceeohFixYxfvx4vvOd73R1WXtkgErAW9/6VhYvXszSpUvZunUrP/nJT3jXu961U5tTTz2Vf/3XfwXgrrvuYvLkyUQE69at46yzzuLLX/4yRx99dFeUv9+yX6rP3n6oacn8+fMBOOSQQ4gITjvtNB5//PEOqL59GaASxWGmadOmccYZZ3D00Udz2mmnMW7cOKZOnfrK+Z2PfOQjrFmzhkmTJvG9732Pyy+/HIAZM2bw7LPPMn36dI4//niOP/54Vq1aBcDll1/O+PHj2bRpE+PHj+ef/umfumwb90X2S/XZmw81u1JbW0tDQwMvvPACUFyC9KY3vanjNqKdRGZ2dQ1dpq6uLmfOnLnX65ly06B2qKY6TDt3bVeXIKnK/eIXv+DSSy9l+/btnH322XzhC19g6tSp1NXVceqpp/LSSy9xwQUXMH/+fAYNGsQPfvADRo0aBcDEiRPZsGED27ZtY8CAAdx55528+c1v5oYbbuC6666jZ8+eHHzwwVxzzTW73WvtSDU1NbMz88g9tTNADdCdGKCSurvWBqijcLXf2l8+2OxPH2r2lz6B/atfVE6nB2hEfAK4GKgFFgKfy8xf76b9CcC3gfHACmBaZl63N+uUJBX8UFNepw4iiogzgauBqUAd8BhwX0SM2EX7Q4B7K+3qgH8EvhsRHyy7TkmS2kNnj8K9CLgxM2dkZn1mfhpoBC7cRfsLgBWZ+elK+xnATcAX92KdkiTttU4L0IjoBUwC7m82637g2F0sdkwL7X8OHBkRPUuuU5KkvdaZ50BfD/QAVjabvhI4eRfLHAQ0f2TCSoq6Xw9EW9cZEecD51fevlhTU9PQmuKrwOuBFzr6h/zg8x39E/Y7Hd4v9kmb+X+lOu1L/1dGtqZRtxuFm5nfB77f1XW0VUQ80Zph1epc9kv1sU+q0/7YL50ZoC8A24GhzaYPBZ7fxTLP76L9y5X1RYl1SpK01zrtHGhmbgVmA6c0m3UKxcjZlszaRfsnMnNbyXVKkrTXOvsQ7reBWyLiceBRilG2w4DrACLiZoDMPKfS/jrgUxFxFfDPwHHAecCHW7vO/cg+d9i5m7Bfqo99Up32u37p9Fv5VW56MIXipgcLgM9n5sOVeb8CyMwTm7Q/AfgOf7mRwjd2cSOFFtcpSVJH6Nb3wpUkqSwfZyZJUgkGqCRJJRigkiSVYIBK2m9ERDT9LnUkBxFVsYgYDoyhuGHEn4GGzPQGEVIr7QjS9A+dOoABWqUi4kLgo8BEYCPwDLAc+A3w75nZEBGvycw/d2GZ3U5E9MnMzV1dh3YWEa8B3g8MAfoCzwEPZeYfu7Qw7dfk/cZuAAAFWUlEQVQM0CoUEYMpAvNbwPco/iicDJwIjKMI0s9n5qKICD9dd46IGATMA34G3Ao8tuN337QfIuLNFI/hW99lxXYjEdEfuB44ieJIzXIggZeAh4BbMvMp/690nojoCRwCLM3MLV1dT0fxHGh1+u/A7zLza5m5OjOfysz/k5lnAB+n+IR9T0S83j8IneojFPdZngQ8DDwTEVdGxNgm4Xkw8C8UT55Q5/gMMBZ4d2YOBc4GrgLmA38DTIuIIf5f6VSfBOYC10XEeyPioIjo0bRBRAyIiFMrYbtPMkCr01agf0RMAIiI3pVnn5KZj1D8gXiJ4o+DOs9bgBuA/wrUAbdT3FZyUUT8pvKovI8Ah2bm4q4rs9t5F3BTZv4WoPKB81bgU8AXKI7a3NKF9XVHZwKPU4zh+HeK+5pPj4i3R8TASpv/Dlyemdu6qMa9ZoBWpx9THIr6XET0z8wtmbm1cp6HzFwG/AkY3pVFdicR0RtYBPwhM/+Ymf+ZmZcARwLvrMz7CvB14BtdVmg3ExEHUNy+84MRMaQyrUdlfMD2yi09LwCGR8TErqy1u6j0wzZgRmZOpni25vUUHzwfBmZGxN8DnwP+o8sKbQeeA60yTYbfvx+4Gqih2NO5luKQyHDgeIpzo4dn5pIuKLNbqoTooMx8vnI4KpsO4oqIE4GZwIjMXN5FZXY7EXE08EOKD57fzsyVzeYfDNQDYzPzuS4osVuJiFrgLGBRZv682bw64GOV+YOAg/flPjFAq1REHAiMAI4FPkDxJBoonnMaFAMjvtI11XU/OwagRMRoYGPTP9JN5n0ZOC8zR3ddpd1L5ajMa4C/BaZSPGHqTuA2YBnFYff/ChyWmUd1VZ3dTUT0ofiA+VLTa3KbjBX4OsU567quqrE9GKBVJCL+C/A/KM7bvABspjhU+wjF5Ss9Kc4p/L/M/F1X1dndNOmXi4A/UjzQvRG4A/hJZm6s/JH4O4rRt/d0WbHdWOVD53kU59aOADZQjBX4LfCPmblPHy7c1+xq1HNE9AXmADdk5j59usMArSIRcSPFY9t+CqyhOHx7OPAmij/c/+Afgc63i36pA95MccnE9My8v8sK7KYiYgCwoekf6coe6WuB1wETKI4W+H+mk7TUJy20eS3FIKN/ycytnVZcBzBAq0RlD2YDxWGNh5tMGwH8FcV5g9HAhzJzTpcV2s3spl+GA0dT7HWOBD5sv3SuiPhnipGej1Ncb/iq624jYlBmrvUa0M7Ryj45MDP/1OnFdQBH4VaPw4BnKS5hAYrzBZm5NDNvB95LcTj3v3VRfd3VrvrlD5l5B8X5tQ3YL50qIj5M8eHlW8BdFJdInB4RYyrn34iI1wE3RMThhmfH20WffCAi3tikT/oAN+24RG9f5x5olaj8w7qH4iYJ5wC/b36bvoj4NPA/M/OILiixW7JfqlNEzAC2A9OA04FzgTcCDcC9wC8pbq5wdWb26qo6u5Pu2CfugVaJyv1V/xfQB7gZOCciDq58it5x4v0Eimve1Ensl+pTufbzWeBPmbk4M7+ZmYcDR1Hcuu9ciku/vos3UOgU3bVP3AOtMpVDG5cB76O4ifwsYBXFvXAbgY9l5vyuq7B7sl+qS+W+xEMr97jtBWxrNpjoTIpbKr41M5/sqjq7k+7YJwZolapcOvEe4DSKofgLgDsy86kuLaybs1+qV2UEbmTm9oj4O4pDhX27uq7ubH/vEwN0HxA+tqwq2S/VKyIuAnpk5vSurkWF/bFPDFBJ+53KEz62+wGneuyPfWKASpJUgqNwJUkqwQCVJKkEA1SSpBIMUEmSSjBAJUkqwQCVJKmE/w+wpU1/Q7L/1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_circuit_length = 0\n",
    "state = '00'\n",
    "shots = 8192\n",
    "\n",
    "result, qc = unencoded_rb(half_circuit_length, state, shots, backend_real)\n",
    "counts = result.get_counts(qc)\n",
    "visualization.plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h9lm1T73PAVf0NUhE7AvMBD7dY9WtVM+S9mdpRPwB1TSCf5uZS+rWHVH77Xq3AOf1UcfTtbe3t7N8+XIAOjo6GDduHGvWrAFg/PjxdHZ2smLFCgDGjBlDd3c3q1evZvv27QB0dXWxZcsWYMIA5UuShtrmzZtZu3YtAG1tbbS1tbFqVTX77NixY+nq6mLlypXs2rULgO7ubtatW8fWrVsB6OzsZOfOnQ3/XiMv1B6sd4a+EBgDbOrRvgmY28c2G6iOTu8H9gXeDtweEUdn5p21PlP6GHNKbwNm5pXAlQAzZszIww575hM4Ay0fdNBBz1ieOnVqH6VLkobTpEmTmDRp0jPaev4PP+SQQ56xPH36dKZPn170e3s0kcJQy8zVwOq6pnsi4o+pXqN2Z2/bSJI0HEomUjghIu6IiEcj4lcR8f2IOL6BTR+legXa5B7tk4GNTZRwL/CyuuWNgzCmJElNaXYihTOpJpR/CPgQMB94GPhmRLyrv20zcyewjGe/N3Qe1d24jTqU6tTubvcMwpiSJDWl2VO4HwIuyMzP17V9KSKWUYXpPw2w/aXAtRFxH3AXcDbV69G+ABAR1wBk5um15fcBPwd+THUN9G3AicDJdWNeDtwREfOBbwFvBI4BXtvkvkmS1LBmA3Qa1cuze7qZZ99d+yyZeUNETAI+TPU854PA8Zm5tm78evsCnwJeRPW+0R8DJ2Tmwrox746I06hmQvo41dHxqZl5bzM7JklSM5oN0HVUp0fX9Gh/HbD22d2fLTOvoI/HXTJzTo/lBcCCBsa8id4ft5EkaUg0G6CfBj5Xe/vK7muMr6F6vOT8wSxMkqRW1uwLtb8YEb8EPkA1+xDAT4BTMvPbg12cJEmtquEAjYh9qE7V3pGZ3xy6kiRJan0NP8aSmU8C3wD2H7pyJEnaOzQ7kcKPgJcORSGSJO1Nmg3QjwGfiYgTI+LFETGx/jME9UmS1JKavQv3X2p/vwFkXXvUlscMRlGSJLW6ZgP0mCGpQpKkvUxDARoR46hmBDoReD6wCHhPPy+5liTpOa3Ra6AXA2dQncK9jmo2on8YopokSWp5jZ7CPQn4n5l5PUBEfBW4KyLGZOZTQ1adJEktqtEj0BdT9wLrzLwPeJLqTSqSJI06jQboGGBnj7Ynaf4mJEmSnhMaDcAAvhIRT9S17QdcFRG/3d2QmW8YzOIkSWpVjQbol3tp+8pgFiJJ0t6koQDNzHcOdSGSJO1Nmp3KT5IkYYBKklTEAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCwx6gEXFuRDwcETsiYllEHNlP35Mi4taI+FVEPBYR90bEG3r0OSMispfPfkO/N5Kk0WpYAzQiTgUuBz4BzADuBm6OiGl9bHI0sBg4odZ/IfDNXkL3t0B7/Sczdwz+HkiSVNlnmH/vAuDqzLyqtnx+RPw5cA5wUc/OmfneHk0XR8QJwInAnc/smhuHomBJknozbEegEbEvMBO4tceqW4HZTQy1P7C1R9vYiFgbEb+IiO9FxIw9KFWSpAEN5xHoC4ExwKYe7ZuAuY0MEBHvBl4EXFvXvBp4F/AjqnB9L3BXRHRn5s96GeMs4CyA9vZ2li9fDkBHRwfjxo1jzZo1AIwfP57Ozk5WrFgBwJgxY+ju7mb16tVs374dgK6uLrZs2QJMaKR8SdIQ2rx5M2vXrgWgra2NtrY2Vq1aBcDYsWPp6upi5cqV7Nq1C4Du7m7WrVvH1q3VMVlnZyc7d+5s+PciMwd5F/r4oYgO4BHg6My8o679o8BbM/OgAbY/mSo4T83M7/bTbwzwALAkM9/T35gzZszIxYsXN7EXvfurLxugkjTSFryj58nJMhMnTlyWmbMG6jecNxE9CjwFTO7RPhno9/plRLyJKjxP7y88ATLzKWAp8LLyUiVJ6t+wBWhm7gSWAfN6rJpHdTduryLiFKrwPCMzbxrodyIigD8BNpRXK0lS/4b7LtxLgWsj4j7gLuBsoAP4AkBEXAOQmafXlk+jCs8LgTsiYkptnJ2ZuaXW56+BHwI/Aw4A3kMVoOcM0z5JkkahYQ3QzLwhIiYBH6Z6XvNB4PjMXFvr0vN50LOparys9tnt+8Cc2vcDgSuBKcCvgRXAUZl531DsgyRJMPxHoGTmFcAVfayb099yH9u8H3j/YNQmSVKjnAtXkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklTAAJUkqYABKklSAQNUkqQCBqgkSQUMUEmSChigkiQVMEAlSSpggEqSVMAAlSSpgAEqSVIBA1SSpAIGqCRJBQxQSZIKGKCSJBUwQCVJKmCASpJUwACVJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFTBAJUkqYIBKklRg2AM0Is6NiIcjYkdELIuIIwfof3St346I+M+IOHtPx5QkaU8Na4BGxKnA5cAngBnA3cDNETGtj/7TgYW1fjOATwKfi4iTS8eUJGkwDPcR6AXA1Zl5VWb+JDPPBzYA5/TR/2xgfWaeX+t/FfBl4MI9GFOSpD02bAEaEfsCM4Fbe6y6FZjdx2ZH9NL/FmBWRDy/cExJkvbYPsP4Wy8ExgCberRvAub2sc0UYFEv/fepjRfNjhkRZwFn1Rb/a+LEiasbKV4aBV4IPDrSRUil/vH9gzbUHzXSaTgDtCVk5pXAlSNdh9RqImJpZs4a6TqkvcVwBuijwFPA5B7tk4GNfWyzsY/+T9bGi4IxJUnaY8N2DTQzdwLLgHk9Vs2junO2N/f00X9pZu4qHFOSpD023KdwLwWujYj7gLuo7rLtAL4AEBHXAGTm6bX+XwDOi4jLgC8CrwHOAN7c6JiSGualDakJwxqgmXlDREwCPgy0Aw8Cx2fm2lqXaT36PxwRxwOfpXosZT3wnsz8ehNjSmpA7f4ASQ2KzBzpGiRJ2us4F64kSQUMUEmSChigkiQVMEAlSSow6mYiklSJiBcBL6WakOR3wOrMdAISqUHehSuNQhFxDvAuoBvYDqwBfgH8EPhWZq6OiOdl5u9GsEyppXkKVxplas9NfwL4NtWz00dQvSbwKeB04O8i4pWZ+buIiJGrVGptHoFKo0xEnA+8LTNf3cu611K9uH4q8KeZ6dtZpD54BCqNPjuB/SPiVQAR8Qe1d+uSmT8A3grsAF43ciVKrc8AlUafm6huGnpfROyfmU9k5s6IeB5AZq4DtgEvGskipVZngEqjSO2a5haquaPnAesj4ksRMbO2flpEvA04BPjayFUqtT6vgUqjUEQcSPXyhtnAG6nedATVe3QDuDYzPzYy1Ul7BwNUGiUi4g+BtwMfoHoh/eNUp2p/QPX4yvOpngv918z86UjVKe0tDFBplIiIq4GDge9SncadSHWq9uXAL4EPZ+a9I1agtJcxQKVRoHbt8zGqd+XeUdc2DXg1cCbQCZySmctHrFBpL+JNRNLo8ErgYapHWADIytrM/BrwP6hO5/7FCNUn7XUMUGl0+E+q07SfjYiX7X5kZbfMfIJqNqLXj0Rx0t7IAJVGgcx8HPg/wFjgGuD0iHhxRLwAICLGAUcDD45cldLexWug0ihSm33oI8AbqCaRvwf4FTAX2ACcmZkrR65Cae9hgEqjUO2RlhOAE6mm7XsQuDEz/2NEC5P2IgaoNMr52jKpjAEqSVIBbyKSJKmAASpJUgEDVJKkAgaoJEkFDFBJkgoYoJIkFfj/ZBykshZXM8UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_circuit_length = 6\n",
    "state = '00'\n",
    "shots = 8192\n",
    "\n",
    "result, qc = unencoded_rb(half_circuit_length, state, shots, backend_sim)\n",
    "counts = result.get_counts(qc)\n",
    "visualization.plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execute experiment over all states and variable circuit length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-280-e5c67e5f7e30>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munencoded_rb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend_real\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m             \u001b[0mqcs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-159-79f8dd9525c9>\u001b[0m in \u001b[0;36munencoded_rb\u001b[0;34m(circuit_length, initial_state, shots, backend)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;31m# Execute quantum circuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshots\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout, wait)\u001b[0m\n\u001b[1;32m    247\u001b[0m             \u001b[0mJobError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mattempted\u001b[0m \u001b[0mto\u001b[0m \u001b[0mrecover\u001b[0m \u001b[0ma\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m         \"\"\"\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_completion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36m_wait_for_completion\u001b[0;34m(self, timeout, wait)\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_final_status_websocket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m                 \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mWebsocketError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36m_wait_for_final_status_websocket\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    625\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    626\u001b[0m             status_response = self._api.job_final_status_websocket(\n\u001b[0;32m--> 627\u001b[0;31m                 self._job_id, timeout=timeout)\n\u001b[0m\u001b[1;32m    628\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    629\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mWebsocketTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api_v2/clients/account.py\u001b[0m in \u001b[0;36mjob_final_status_websocket\u001b[0;34m(self, job_id, timeout)\u001b[0m\n\u001b[1;32m    227\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m         return loop.run_until_complete(\n\u001b[0;32m--> 229\u001b[0;31m             self.client_ws.get_job_status(job_id, timeout=timeout))\n\u001b[0m\u001b[1;32m    230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mjob_properties\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m     58\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_log_destroy_pending\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m                 \u001b[0mrun_once\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/nest_asyncio.py\u001b[0m in \u001b[0;36mrun_once\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0mtimeout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m         \u001b[0mevent_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     90\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_process_events\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevent_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    575\u001b[0m             \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m                 \u001b[0mkev_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_kqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_ev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mready\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "half_circuit_lengths = np.arange(0, 50, 5)\n",
    "states = ['00', '01', '10', '11']\n",
    "shots = 8192\n",
    "n_reps = 5\n",
    "\n",
    "# Initialize dictionaries that will save results\n",
    "results = {}\n",
    "qcs = {}\n",
    "\n",
    "for i in tnrange(n_reps, desc='rep'):\n",
    "    results[i] = {}\n",
    "    qcs[i] = {}\n",
    "    for half_circuit_length in half_circuit_lengths:\n",
    "        results[i][half_circuit_length] = {}\n",
    "        qcs[i][half_circuit_length] = {}\n",
    "        for state in states:\n",
    "            result, qc = unencoded_rb(half_circuit_length, state, shots, backend_real)\n",
    "            results[i][half_circuit_length][state] = result\n",
    "            qcs[i][half_circuit_length][state] = qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organize output dictionaries into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store results in pandas dataframe\n",
    "\n",
    "columns = ['cir_len', 'init_state', '00', '01', '10', '11']\n",
    "df = pd.DataFrame(columns=columns)\n",
    "for i in results.keys():\n",
    "    for half_circuit_length in results[i].keys():\n",
    "        for state, res in results[i][half_circuit_length].items():\n",
    "            counts = res.get_counts()\n",
    "            # build dataframe row from counts dictionary\n",
    "            length = half_circuit_length * 2 - 1\n",
    "            if length < 0:\n",
    "                length = 0\n",
    "            df_row = counts.copy()\n",
    "            df_row['cir_len'] = length\n",
    "            df_row['init_state'] = state\n",
    "            if df.empty:\n",
    "                df = pd.DataFrame(data=df_row, index=[0])\n",
    "            else:\n",
    "                df = df.append(df_row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1093</td>\n",
       "      <td>4013</td>\n",
       "      <td>1308</td>\n",
       "      <td>1778</td>\n",
       "      <td>20</td>\n",
       "      <td>00</td>\n",
       "      <td>0.489868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1226</td>\n",
       "      <td>1541</td>\n",
       "      <td>1311</td>\n",
       "      <td>4114</td>\n",
       "      <td>20</td>\n",
       "      <td>01</td>\n",
       "      <td>0.502197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     10    00    11    01  cir_len init_state     q_ave\n",
       "0  1145  4095  1500  1452       10         00  0.499878\n",
       "1  1059  1845  1054  4234       10         01  0.516846\n",
       "2  4331  1397  1099  1365       10         10  0.528687\n",
       "3  1430  1260  4191  1311       10         11  0.511597\n",
       "4  1093  4013  1308  1778       20         00  0.489868\n",
       "5  1226  1541  1311  4114       20         01  0.502197"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filename = './unencoded_data.pkl'\n",
    "# df.to_pickle(filename)\n",
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute average survival $\\bar{q}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Got to be a better way to do this\n",
    "df['q_ave'] = np.diag(df[df.loc[:,'init_state']].values) / shots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10</th>\n",
       "      <th>00</th>\n",
       "      <th>11</th>\n",
       "      <th>01</th>\n",
       "      <th>cir_len</th>\n",
       "      <th>init_state</th>\n",
       "      <th>q_ave</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1145</td>\n",
       "      <td>4095</td>\n",
       "      <td>1500</td>\n",
       "      <td>1452</td>\n",
       "      <td>10</td>\n",
       "      <td>00</td>\n",
       "      <td>0.499878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1059</td>\n",
       "      <td>1845</td>\n",
       "      <td>1054</td>\n",
       "      <td>4234</td>\n",
       "      <td>10</td>\n",
       "      <td>01</td>\n",
       "      <td>0.516846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4331</td>\n",
       "      <td>1397</td>\n",
       "      <td>1099</td>\n",
       "      <td>1365</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.528687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1430</td>\n",
       "      <td>1260</td>\n",
       "      <td>4191</td>\n",
       "      <td>1311</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>0.511597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1093</td>\n",
       "      <td>4013</td>\n",
       "      <td>1308</td>\n",
       "      <td>1778</td>\n",
       "      <td>20</td>\n",
       "      <td>00</td>\n",
       "      <td>0.489868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     10    00    11    01  cir_len init_state     q_ave\n",
       "0  1145  4095  1500  1452       10         00  0.499878\n",
       "1  1059  1845  1054  4234       10         01  0.516846\n",
       "2  4331  1397  1099  1365       10         10  0.528687\n",
       "3  1430  1260  4191  1311       10         11  0.511597\n",
       "4  1093  4013  1308  1778       20         00  0.489868"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Fidelity parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho00_cir_lens = df[df['init_state'] == '00'].sort_values(by='cir_len')['cir_len'].values\n",
    "rho00_q_aves = df[df['init_state'] == '00'].sort_values(by='cir_len')['q_ave'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit parameters: [0.35600665 0.97624069]\n",
      "variances: [7.31860184e-05 6.19607515e-07]\n"
     ]
    },
    {
     "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": [
    "def exp(x, B, b):\n",
    "    return 0.25 + B * b**x\n",
    "\n",
    "guess = [0.953, .356]\n",
    "popt, pcov = curve_fit(exp, rho00_cir_lens, rho00_q_aves, p0=guess)\n",
    "print('fit parameters:', popt)\n",
    "print('variances:', np.diag(pcov))\n",
    "B = popt[0]\n",
    "b = popt[1]\n",
    "\n",
    "x = np.arange(0, 100)\n",
    "plt.plot(x, exp(x, *popt), '-r')\n",
    "plt.scatter(rho00_cir_lens, rho00_q_aves)\n",
    "plt.title('rho 00 for 2 physical qubits')\n",
    "plt.xlabel('Circuit length')\n",
    "plt.ylabel('Survival probability')\n",
    "plt.legend(['fit','data'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "outputs": [],
   "source": [
    "b_std = np.sqrt(pcov[1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_aves_all = df.sort_values(by=['cir_len'])['q_ave'].values\n",
    "cir_lens_all = df.sort_values(by=['cir_len'])['cir_len'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit parameters: [0.04391206 0.83915374]\n",
      "variances: [0.03763829 0.12557221]\n",
      "little c: 0.8391537430536916\n"
     ]
    },
    {
     "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": [
    "def double_exp(x, B, b, C, c):\n",
    "    return 0.25 + B * b**x + C * c**x\n",
    "\n",
    "guess = [0.05, 0.356]\n",
    "popt, pcov = curve_fit(lambda x, C, c: double_exp(x, B, b, C, c), cir_lens_all, q_aves_all, p0=guess, bounds=([0,0],[1,1]))\n",
    "print('fit parameters:', popt)\n",
    "print('variances:', np.diag(pcov))\n",
    "c = popt[1]\n",
    "print('little c:', c)\n",
    "\n",
    "x = np.arange(0, 100)\n",
    "plt.plot(x, double_exp(x, *[B,b,*popt]), '-m')\n",
    "plt.scatter(cir_lens_all, q_aves_all, color='r', s=10)\n",
    "plt.title('Real RB with two physical qubits')\n",
    "plt.xlabel('circuit length')\n",
    "plt.ylabel('survival probability')\n",
    "plt.legend(['fit','data'])\n",
    "plt.savefig('survival_unencoded.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3543616889565039"
      ]
     },
     "execution_count": 559,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_std = np.sqrt(pcov[1,1])\n",
    "c_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0007871515198468396"
      ]
     },
     "execution_count": 560,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infidelity: 5.89456 %\n"
     ]
    }
   ],
   "source": [
    "fidelity = (9*b + 6*c + 5) / 20\n",
    "print('Infidelity:', round((1 - fidelity)*100, 5), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0477171587565448 0.1656082909852199\n"
     ]
    }
   ],
   "source": [
    "print((9*(b+b_std) + 6*(c+c_std)+ 5) / 20, 1 - (9*(b-b_std) + 6*(c-c_std) + 5) / 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoded Randomized Benchmark with R(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_state_enc(state, qubit_map, qc, qr):\n",
    "    qc.barrier()\n",
    "    # initialize '00' state\n",
    "    qc.h(qr[qubit_map[1]])\n",
    "    qc.cx(qr[qubit_map[1]],qr[qubit_map[0]])\n",
    "    qc.cx(qr[qubit_map[1]],qr[qubit_map[2]])\n",
    "    qc.cx(qr[qubit_map[2]],qr[qubit_map[3]])\n",
    "    qc.barrier()\n",
    "    if state == '01':\n",
    "        qc.x(qr[qubit_map[0]])\n",
    "        qc.x(qr[qubit_map[1]])\n",
    "    elif state == '10':\n",
    "        qc.x(qr[qubit_map[0]])\n",
    "        qc.x(qr[qubit_map[2]])\n",
    "    elif state == '11':\n",
    "        qc.x(qr[qubit_map[1]])\n",
    "        qc.x(qr[qubit_map[2]])\n",
    "    qc.barrier()\n",
    "\n",
    "def swap_enc(qubit_map, q_i, q_j):\n",
    "    new_qubit_map = {}\n",
    "    new_qubit_map[q_i] = qubit_map[q_j]\n",
    "    new_qubit_map[q_j] = qubit_map[q_i]\n",
    "    return new_qubit_map\n",
    "\n",
    "def addGate_enc(i, qubit_map, qr, qc, inverse=False):\n",
    "    if i == '1':\n",
    "        qc.x(qr[qubit_map[2]])\n",
    "        qc.x(qr[qubit_map[3]])\n",
    "    elif i == '2':\n",
    "        qc.x(qr[qubit_map[0]])\n",
    "        qc.x(qr[qubit_map[2]])\n",
    "    elif i == '3':\n",
    "        qc.z(qr[qubit_map[2]])\n",
    "        qc.z(qr[qubit_map[3]])\n",
    "    elif i == '4':\n",
    "        qc.z(qr[qubit_map[0]])\n",
    "        qc.z(qr[qubit_map[2]])\n",
    "    elif i == '5':\n",
    "        qc.h(0)\n",
    "        qc.h(1)\n",
    "        qc.h(2)\n",
    "        qc.h(3)\n",
    "    elif i == '6':\n",
    "        if inverse:\n",
    "            qc.sdg(0)\n",
    "            qc.sdg(1)\n",
    "            qc.sdg(2)\n",
    "            qc.sdg(3)\n",
    "        else:\n",
    "            qc.s(0)\n",
    "            qc.s(1)\n",
    "            qc.s(2)\n",
    "            qc.s(3)\n",
    "    elif i == '7':\n",
    "        qubit_map = swap_enc(qubit_map, 2, 3)\n",
    "    elif i == '8':\n",
    "        qubit_map = swap_enc(qubit_map, 1, 3)\n",
    "    else:\n",
    "        print(' --- Error ---\\nExceeded gate range')\n",
    "    qc.barrier()\n",
    "\n",
    "# Add random set of gates selected by randR2Gates. Includes gate inverses as well\n",
    "def addRandGates_enc(rand_gate_indices, qubit_map, qr, qc):\n",
    "    # Add gates based on random index\n",
    "    for gate_indices in rand_gate_indices:\n",
    "        for gate in gate_indices:\n",
    "            addGate_enc(gate, qubit_map, qr, qc)\n",
    "    # Add inverse of gates\n",
    "    for gate_indices in rand_gate_indices[::-1]:\n",
    "        for gate in gate_indices:\n",
    "            addGate_enc(gate, qubit_map, qr, qc, inverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "def encoded_rb(half_circuit_length, initial_state, shots, backend):\n",
    "    n_qubits = 4\n",
    "    qubit_map = {}  # used to track virtual swaps\n",
    "    for i in range(n_qubits):\n",
    "        qubit_map[i] = i\n",
    "\n",
    "    # Initialize quantum circuit and registers\n",
    "    qr = QuantumRegister(n_qubits)\n",
    "    cr = ClassicalRegister(n_qubits)\n",
    "    qc = QuantumCircuit(qr, cr)\n",
    "    \n",
    "    # Initialize quantum state\n",
    "    init_state_enc(initial_state, qubit_map, qc, qr)\n",
    "    # Add randomized gates (along with inverses)\n",
    "    rand_gate_indices = randR2Gates(half_circuit_length)\n",
    "    addRandGates_enc(rand_gate_indices, qubit_map, qr, qc)\n",
    "    # Measure qubits\n",
    "    for i in range(n_qubits):\n",
    "        qc.measure(i, i)\n",
    "\n",
    "    # Execute quantum circuit\n",
    "    result = execute(qc, backend, shots=shots).result()\n",
    "    return result, qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test runs "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_circuit_length = 4\n",
    "state = '11'\n",
    "shots = 8192\n",
    "\n",
    "result, qc = encoded_rb(half_circuit_length, state, shots, backend_sim)\n",
    "counts = result.get_counts(qc)\n",
    "visualization.plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BackendStatus(backend_name='ibmq_burlington', backend_version='1.1.4', operational=True, pending_jobs=3, status_msg='active')"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter backends to find the least busy 5 qubit quantum computer and check current \"pending_jobs\"\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "smaller_qcs = provider.backends(filters=lambda x: x.configuration().n_qubits == 5 and not x.configuration().simulator)\n",
    "backend_real = least_busy(smaller_qcs)\n",
    "\n",
    "# Check status and \"pending_jobs\" to see how long it might take to run\n",
    "backend_real.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "half_circuit_length = 2\n",
    "state = '00'\n",
    "shots = 8192\n",
    "\n",
    "result, qc = encoded_rb(half_circuit_length, state, shots, backend_real)\n",
    "counts = result.get_counts(qc)\n",
    "visualization.plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Execute experiment over all states and variable circuit length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n",
      "Error checking job status using websocket, retrying using HTTP.\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-352-65eb7cb6ccd0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mqcs_en\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m             \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoded_rb\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend_real\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m             \u001b[0mresults_en\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mqcs_en\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhalf_circuit_length\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mqc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-345-231849aa9848>\u001b[0m in \u001b[0;36mencoded_rb\u001b[0;34m(half_circuit_length, initial_state, shots, backend)\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;31m# Execute quantum circuit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mqc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshots\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout, wait)\u001b[0m\n\u001b[1;32m    247\u001b[0m             \u001b[0mJobError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mattempted\u001b[0m \u001b[0mto\u001b[0m \u001b[0mrecover\u001b[0m \u001b[0ma\u001b[0m \u001b[0mresult\u001b[0m \u001b[0mon\u001b[0m \u001b[0ma\u001b[0m \u001b[0mfailed\u001b[0m \u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    248\u001b[0m         \"\"\"\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_completion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    251\u001b[0m         \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36m_wait_for_completion\u001b[0;34m(self, timeout, wait)\u001b[0m\n\u001b[1;32m    566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    567\u001b[0m         \u001b[0;31m# Use traditional http requests if websocket not available or failed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 568\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wait_for_final_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wait_for_submission\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m60\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36m_wait_for_final_status\u001b[0;34m(self, timeout, wait)\u001b[0m\n\u001b[1;32m    599\u001b[0m         \"\"\"\n\u001b[1;32m    600\u001b[0m         \u001b[0mstart_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mJOB_FINAL_STATES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m             \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/job/ibmqjob.py\u001b[0m in \u001b[0;36mstatus\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    310\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    311\u001b[0m             \u001b[0;31m# TODO: See result values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 312\u001b[0;31m             \u001b[0mapi_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_status_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_job_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    313\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api_v2/clients/account.py\u001b[0m in \u001b[0;36mget_status_job\u001b[0;34m(self, id_job)\u001b[0m\n\u001b[1;32m    293\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_status_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid_job\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m         \u001b[0;31m# pylint: disable=missing-docstring\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 295\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjob_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mid_job\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    296\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0msubmit_job\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mqobj_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackend_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api_v2/clients/account.py\u001b[0m in \u001b[0;36mjob_status\u001b[0;34m(self, job_id)\u001b[0m\n\u001b[1;32m    199\u001b[0m             \u001b[0mdict\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mjob\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \"\"\"\n\u001b[0;32m--> 201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mjob_final_status_websocket\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjob_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api_v2/rest/job.py\u001b[0m in \u001b[0;36mstatus\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     95\u001b[0m         \u001b[0;34m\"\"\"Return the status of a job.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'status'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 97\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     98\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupload_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, url, **kwargs)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 546\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GET'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/qiskit/providers/ibmq/api_v2/session.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, bare, **kwargs)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 148\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    149\u001b[0m             \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mRequestException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    531\u001b[0m         }\n\u001b[1;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0;31m# Send the request\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madapter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    648\u001b[0m         \u001b[0;31m# Total elapsed time of the request (approximately)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/requests/adapters.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m    447\u001b[0m                     \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    448\u001b[0m                     \u001b[0mretries\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_retries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                     \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    450\u001b[0m                 )\n\u001b[1;32m    451\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m    670\u001b[0m                 \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m                 \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 672\u001b[0;31m                 \u001b[0mchunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mchunked\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    673\u001b[0m             )\n\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    419\u001b[0m                     \u001b[0;31m# Python 3 (including for exceptions like SystemExit).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m                     \u001b[0;31m# Otherwise it looks like a bug in the code.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m                     \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSocketTimeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBaseSSLError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSocketError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    423\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mread_timeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/packages/six.py\u001b[0m in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/connectionpool.py\u001b[0m in \u001b[0;36m_make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m    414\u001b[0m                 \u001b[0;31m# Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    415\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m                     \u001b[0mhttplib_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m                     \u001b[0;31m# Remove the TypeError from the exception chain in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1329\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1330\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1331\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1332\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1333\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 297\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    298\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0mline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_MAXLINE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0m_MAXLINE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mLineTooLong\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"status line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/urllib3/contrib/pyopenssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    311\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mOpenSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSysCallError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Unexpected EOF\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/OpenSSL/SSL.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1819\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_peek\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1820\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1821\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSSL_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1822\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_raise_ssl_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_ssl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "half_circuit_lengths_en = np.arange(0, 50, 5)\n",
    "states = ['00', '01', '10', '11']\n",
    "shots = 8192\n",
    "n_reps = 5\n",
    "\n",
    "# Initialize dictionaries that will save results\n",
    "results_en = {}\n",
    "qcs_en = {}\n",
    "\n",
    "for i in tnrange(n_reps, dsec='reps'):\n",
    "    results_en[i] = {}\n",
    "    qcs_en[i] = {}\n",
    "    for half_circuit_length in half_circuit_lengths_en:\n",
    "        results_en[i][half_circuit_length] = {}\n",
    "        qcs_en[i][half_circuit_length] = {}\n",
    "        for state in states:\n",
    "            result, qc = encoded_rb(half_circuit_length, state, shots, backend_real)\n",
    "            results_en[i][half_circuit_length][state] = result\n",
    "            qcs_en[i][half_circuit_length][state] = qc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Organize output dictionaries into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store results in pandas dataframe\n",
    "columns = ['cir_len', 'init_state', '0000', '0001', '0010', '0100', '1000', '0011', '0101', '1001', '0110', '1010', '1100', '0111', '1011', '1101', '1110', '1111']\n",
    "df_en = pd.DataFrame(columns=columns)\n",
    "for i in results_en.keys():\n",
    "    for half_circuit_length in results_en[i].keys():\n",
    "        for state, res in results_en[i][half_circuit_length].items():\n",
    "            counts = res.get_counts()\n",
    "            # build dataframe row from counts dictionary\n",
    "            df_row = counts.copy()\n",
    "            length = half_circuit_length * 2 - 1\n",
    "            if length < 0:\n",
    "                length = 0\n",
    "            df_row['cir_len'] = length\n",
    "            df_row['init_state'] = state\n",
    "            if df_en.empty:\n",
    "                df_en = pd.DataFrame(data=df_row, index=[0])\n",
    "            else:\n",
    "                df_en = df_en.append(df_row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2])"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_en.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0011</th>\n",
       "      <th>1001</th>\n",
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       "      <th>0001</th>\n",
       "      <th>0110</th>\n",
       "      <th>1100</th>\n",
       "      <th>1101</th>\n",
       "      <th>1110</th>\n",
       "      <th>1111</th>\n",
       "      <th>1011</th>\n",
       "      <th>cir_len</th>\n",
       "      <th>init_state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>276</td>\n",
       "      <td>51</td>\n",
       "      <td>294</td>\n",
       "      <td>226</td>\n",
       "      <td>491</td>\n",
       "      <td>2090</td>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>3078</td>\n",
       "      <td>312</td>\n",
       "      <td>105</td>\n",
       "      <td>282</td>\n",
       "      <td>241</td>\n",
       "      <td>175</td>\n",
       "      <td>490</td>\n",
       "      <td>59</td>\n",
       "      <td>126</td>\n",
       "      <td>582</td>\n",
       "      <td>116</td>\n",
       "      <td>1864</td>\n",
       "      <td>310</td>\n",
       "      <td>100</td>\n",
       "      <td>135</td>\n",
       "      <td>217</td>\n",
       "      <td>0</td>\n",
       "      <td>01</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>281</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
       "      <td>376</td>\n",
       "      <td>314</td>\n",
       "      <td>227</td>\n",
       "      <td>2088</td>\n",
       "      <td>3332</td>\n",
       "      <td>371</td>\n",
       "      <td>105</td>\n",
       "      <td>125</td>\n",
       "      <td>145</td>\n",
       "      <td>189</td>\n",
       "      <td>71</td>\n",
       "      <td>238</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138</td>\n",
       "      <td>1915</td>\n",
       "      <td>203</td>\n",
       "      <td>150</td>\n",
       "      <td>550</td>\n",
       "      <td>381</td>\n",
       "      <td>269</td>\n",
       "      <td>173</td>\n",
       "      <td>201</td>\n",
       "      <td>266</td>\n",
       "      <td>3121</td>\n",
       "      <td>359</td>\n",
       "      <td>90</td>\n",
       "      <td>243</td>\n",
       "      <td>51</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156</td>\n",
       "      <td>208</td>\n",
       "      <td>311</td>\n",
       "      <td>2347</td>\n",
       "      <td>509</td>\n",
       "      <td>312</td>\n",
       "      <td>157</td>\n",
       "      <td>252</td>\n",
       "      <td>504</td>\n",
       "      <td>454</td>\n",
       "      <td>135</td>\n",
       "      <td>253</td>\n",
       "      <td>381</td>\n",
       "      <td>399</td>\n",
       "      <td>1440</td>\n",
       "      <td>374</td>\n",
       "      <td>9</td>\n",
       "      <td>00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2519</td>\n",
       "      <td>267</td>\n",
       "      <td>536</td>\n",
       "      <td>121</td>\n",
       "      <td>223</td>\n",
       "      <td>381</td>\n",
       "      <td>520</td>\n",
       "      <td>173</td>\n",
       "      <td>164</td>\n",
       "      <td>289</td>\n",
       "      <td>259</td>\n",
       "      <td>1738</td>\n",
       "      <td>315</td>\n",
       "      <td>197</td>\n",
       "      <td>270</td>\n",
       "      <td>220</td>\n",
       "      <td>9</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0011  1001  0111  0000  0100  1000  0010  1010  0101  0001  0110  1100  \\\n",
       "0    98    33   172  3821   141   140   131    59    44   276    51   294   \n",
       "1  3078   312   105   282   241   175   490    59   126   582   116  1864   \n",
       "2   100   281   115   115   376   314   227  2088  3332   371   105   125   \n",
       "3   138  1915   203   150   550   381   269   173   201   266  3121   359   \n",
       "4   156   208   311  2347   509   312   157   252   504   454   135   253   \n",
       "5  2519   267   536   121   223   381   520   173   164   289   259  1738   \n",
       "\n",
       "   1101  1110  1111  1011  cir_len init_state  \n",
       "0   226   491  2090   125        0         00  \n",
       "1   310   100   135   217        0         01  \n",
       "2   145   189    71   238        0         10  \n",
       "3    90   243    51    82        0         11  \n",
       "4   381   399  1440   374        9         00  \n",
       "5   315   197   270   220        9         01  "
      ]
     },
     "execution_count": 515,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filename = './encoded_data.pkl'\n",
    "# df_en.to_pickle(filename)\n",
    "df_en.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove states not part of the codespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1100</th>\n",
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       "      <th>cir_len</th>\n",
       "      <th>init_state</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>11</td>\n",
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       "      <th>4</th>\n",
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       "      <td>9</td>\n",
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       "      <th>5</th>\n",
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       "      <td>121</td>\n",
       "      <td>173</td>\n",
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       "      <td>1738</td>\n",
       "      <td>270</td>\n",
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      ],
      "text/plain": [
       "   0011  1001  0000  1010  0101  0110  1100  1111  cir_len init_state\n",
       "0    98    33  3821    59    44    51   294  2090        0         00\n",
       "1  3078   312   282    59   126   116  1864   135        0         01\n",
       "2   100   281   115  2088  3332   105   125    71        0         10\n",
       "3   138  1915   150   173   201  3121   359    51        0         11\n",
       "4   156   208  2347   252   504   135   253  1440        9         00\n",
       "5  2519   267   121   173   164   259  1738   270        9         01"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "codespace = ['0000', '1111', '1100', '0011', '0101', '1010', '1001', '0110']\n",
    "new_columns = df_en.columns\n",
    "df_en_codespace = df_en[new_columns[np.isin(new_columns, codespace + ['cir_len','init_state'])]]\n",
    "df_en_codespace.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute average survival $\\bar{q}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Razander/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/Users/Razander/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/Users/Razander/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "/Users/Razander/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0011</th>\n",
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       "      <th>0101</th>\n",
       "      <th>0110</th>\n",
       "      <th>1100</th>\n",
       "      <th>1111</th>\n",
       "      <th>cir_len</th>\n",
       "      <th>init_state</th>\n",
       "      <th>state1</th>\n",
       "      <th>state2</th>\n",
       "      <th>shots</th>\n",
       "      <th>q_ave</th>\n",
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       "  </thead>\n",
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       "      <th>2</th>\n",
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       "      <td>2088</td>\n",
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       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3332</td>\n",
       "      <td>2088</td>\n",
       "      <td>6217</td>\n",
       "      <td>0.871803</td>\n",
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       "      <td>6108</td>\n",
       "      <td>0.824492</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>156</td>\n",
       "      <td>208</td>\n",
       "      <td>2347</td>\n",
       "      <td>252</td>\n",
       "      <td>504</td>\n",
       "      <td>135</td>\n",
       "      <td>253</td>\n",
       "      <td>1440</td>\n",
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       "      <td>00</td>\n",
       "      <td>2347</td>\n",
       "      <td>1440</td>\n",
       "      <td>5295</td>\n",
       "      <td>0.715203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2519</td>\n",
       "      <td>267</td>\n",
       "      <td>121</td>\n",
       "      <td>173</td>\n",
       "      <td>164</td>\n",
       "      <td>259</td>\n",
       "      <td>1738</td>\n",
       "      <td>270</td>\n",
       "      <td>9</td>\n",
       "      <td>01</td>\n",
       "      <td>1738</td>\n",
       "      <td>2519</td>\n",
       "      <td>5511</td>\n",
       "      <td>0.772455</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0011  1001  0000  1010  0101  0110  1100  1111  cir_len init_state  state1  \\\n",
       "0    98    33  3821    59    44    51   294  2090        0         00    3821   \n",
       "1  3078   312   282    59   126   116  1864   135        0         01    1864   \n",
       "2   100   281   115  2088  3332   105   125    71        0         10    3332   \n",
       "3   138  1915   150   173   201  3121   359    51        0         11    3121   \n",
       "4   156   208  2347   252   504   135   253  1440        9         00    2347   \n",
       "5  2519   267   121   173   164   259  1738   270        9         01    1738   \n",
       "\n",
       "   state2  shots     q_ave  \n",
       "0    2090   6490  0.910786  \n",
       "1    3078   5972  0.827528  \n",
       "2    2088   6217  0.871803  \n",
       "3    1915   6108  0.824492  \n",
       "4    1440   5295  0.715203  \n",
       "5    2519   5511  0.772455  "
      ]
     },
     "execution_count": 699,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Got to be a better way to do this\n",
    "state_map = {'00':['0000','1111'], '01':['1100','0011'], '10':['0101','1010'], '11':['0110','1001']}\n",
    "state_map1 = {'00':'0000', '01':'1100', '10':'0101', '11':'0110'}\n",
    "state_map2 = {'00':'1111', '01':'0011', '10':'1010', '11':'1001'}\n",
    "\n",
    "# Compare superposition of states. add two states associated with logical state and divide by number of shots in coding space\n",
    "temp = df_en_codespace.loc[:,'init_state'].map(state_map1)\n",
    "df_en_codespace['state1'] = np.diag(df_en_codespace[temp].values)\n",
    "temp = df_en_codespace.loc[:,'init_state'].map(state_map2)\n",
    "df_en_codespace['state2'] = np.diag(df_en_codespace[temp].values)\n",
    "df_en_codespace['shots'] = df_en_codespace[codespace].sum(axis=1)\n",
    "df_en_codespace['q_ave'] = df_en_codespace[['state1','state2']].sum(axis=1) / df_en_codespace['shots']\n",
    "\n",
    "df_en_codespace.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit Fidelity parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_aves_all_enc = df_en_codespace.sort_values(by=['cir_len'])['q_ave'].values\n",
    "cir_lens_all_enc = df_en_codespace.sort_values(by=['cir_len'])['cir_len'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fit parameters: [0.47956885 0.99520946 0.06321915 0.99520976]\n",
      "variances: [1.14133695e+13 6.11481701e+03 1.14133695e+13 3.52026765e+05]\n"
     ]
    },
    {
     "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": [
    "def double_exp(x, B, b, C, c):\n",
    "    return 0.25 + B * b**x + C * c**x\n",
    "\n",
    "guess = [0.5, 0.90, 0.1, 0.9]\n",
    "# popt_enc, pcov_enc = curve_fit(lambda x, C_enc, c_enc: double_exp(x, B_enc, b_enc, C_enc, c_enc), cir_lens_all_enc, q_aves_all_enc, p0=guess, bounds=([0, 0], [1,1]))\n",
    "popt_enc, pcov_enc = curve_fit(double_exp, cir_lens_all_enc, q_aves_all_enc, p0=guess, bounds=([0,0,0,0], [1,1,1,1]))\n",
    "print('fit parameters:', popt_enc)\n",
    "print('variances:', np.diag(pcov_enc))\n",
    "B_enc = popt_enc[0]\n",
    "b_enc = popt_enc[1]\n",
    "C_enc = popt_enc[2]\n",
    "c_enc = popt_enc[3]\n",
    "\n",
    "x = np.arange(0, 1000) / 10\n",
    "# plt.plot(x, double_exp(x, *[B_enc, b_enc, *popt_enc]), '-r')\n",
    "plt.plot(x, double_exp(x, *popt_enc), '-c')\n",
    "plt.scatter(cir_lens_all_enc, q_aves_all_enc, color='b', s=10)\n",
    "plt.title('Real RB with two logical qubits')\n",
    "plt.xlabel('circuit length')\n",
    "plt.ylabel('survival probability')\n",
    "plt.legend(['fit','data'])\n",
    "plt.savefig('survival_encoded.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9952094564962237, 0.9952097566148572)"
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b_enc, c_enc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infidelity: 0.35928 %\n"
     ]
    }
   ],
   "source": [
    "fidelity = (9*b_enc + 6*c_enc + 5) / 20\n",
    "print('Infidelity:', round((1 - fidelity)*100, 5), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison between encoded and unencoded RB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "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": [
    "x = np.arange(0, 1000) / 10\n",
    "plt.plot(x, double_exp(x, *popt_enc), '-c')\n",
    "plt.plot(x, double_exp(x, *[B, b, *popt]), '-m')\n",
    "plt.scatter(cir_lens_all_enc, q_aves_all_enc, color='b', s=10)\n",
    "plt.scatter(cir_lens_all, q_aves_all, color='r', s=10)\n",
    "plt.title('Encoded vs Unencoded')\n",
    "plt.xlabel('circuit length')\n",
    "plt.ylabel('survival probability')\n",
    "plt.legend(['encoded fit','unencoded fit','encoded data','unencoded data'])\n",
    "plt.savefig('survival_comparison.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Circuit Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=452x353 at 0x13AC257B8>"
      ]
     },
     "execution_count": 514,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Logical |00> state preparation\n",
    "n_qubits = 4\n",
    "\n",
    "# Initialize quantum circuit and registers\n",
    "qc = QuantumCircuit(4,4)\n",
    "\n",
    "# initialize '00' state\n",
    "qc.h(1)\n",
    "qc.cx(1, 0)\n",
    "qc.cx(1, 2)\n",
    "qc.cx(2, 3)\n",
    "\n",
    "qc.draw(filename='psi_00_prep.png',output='latex', plot_barriers=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# References\n",
    "[1](https://arxiv.org/pdf/1806.02359v4.pdf) Fault-Tolerant Logical Gates in the IBM Quantum Experience. R. Harper1 and S. T. Flammia. (2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}