TestRebalancing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Qiskit includes\n",
    "from qiskit import QuantumRegister, ClassicalRegister\n",
    "from qiskit import QuantumCircuit, execute\n",
    "from qiskit import Aer\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.providers.aer.noise.errors.readout_error import *\n",
    "from qiskit.circuit import Instruction\n",
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal,\n",
    "                                                 CompleteMeasFitter, \n",
    "                                                 MeasurementFilter)\n",
    "#Other python tools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "from scipy.optimize import minimize\n",
    "import seaborn as sns\n",
    "import matplotlib.lines as mlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Unfolding Methods\n",
    "\n",
    "see https://arxiv.org/abs/1910.00129"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MatrixInversion(ymes,Rin):\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    return np.ravel(np.matmul(np.linalg.inv(np.matrix(Rin)),ymes))\n",
    "\n",
    "def fun(x,ymes,Rin):\n",
    "    mat_dot_x = np.ravel(np.matmul(Rin,x))\n",
    "    return sum((ymes - mat_dot_x)**2)\n",
    "\n",
    "def Ignis(ymes,Rin):\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    #This is copied from https://github.com/Qiskit/qiskit-ignis/blob/master/qiskit/ignis/mitigation/measurement/filters.py\n",
    "    x0 = np.random.rand(len(ymes))\n",
    "    x0 = x0 / sum(x0)\n",
    "    nshots = sum(ymes)\n",
    "    cons = ({'type': 'eq', 'fun': lambda x: nshots - sum(x)})\n",
    "    bnds = tuple((0, nshots) for x in x0)\n",
    "    res = minimize(fun, x0, method='SLSQP',constraints=cons, bounds=bnds, tol=1e-6, args=(ymes,Rin))\n",
    "    return res.x\n",
    "\n",
    "def IBU(ymes,t0,Rin,n):\n",
    "    #This is the iterative Bayesian unfolding method.\n",
    "    #Rin is a matrix where the first coordinate is the measured value and the second coordinate is the true value.\n",
    "    #n is the number of iterations.\n",
    "    \n",
    "    tn = t0\n",
    "    for q in range(n):\n",
    "        out = []\n",
    "        for j in range(len(t0)):\n",
    "            mynum = 0.\n",
    "            for i in range(len(ymes)):\n",
    "                myden = 0.\n",
    "                for k in range(len(t0)):\n",
    "                    myden+=Rin[i][k]*tn[k]\n",
    "                    pass\n",
    "                mynum+=Rin[i][j]*tn[j]*ymes[i]/myden\n",
    "                pass\n",
    "            out+=[mynum]\n",
    "        tn = out\n",
    "        pass\n",
    "    '''\n",
    "    tn = t0\n",
    "    for i in range(n):\n",
    "        Rjitni = [np.array(Rin[:][i])*tn[i] for i in range(len(tn))]\n",
    "        Pm_given_t = Rjitni / np.matmul(Rin,tn)\n",
    "        tn = np.dot(Pm_given_t,ymes)\n",
    "        pass\n",
    "    '''\n",
    "    return tn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Useful functions for converting between bitstrings and integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mybin(input,q,r=1):\n",
    "  if (r==0):\n",
    "    return bin(input).split('b')[1].zfill(q) \n",
    "  else:\n",
    "    return bin(input).split('b')[1].zfill(q)[::-1]\n",
    "\n",
    "nqubits = 5\n",
    "\n",
    "xvals = []\n",
    "xlabs = []\n",
    "xlabs0 = []\n",
    "for i in range(2**nqubits):\n",
    "    xvals+=[i]\n",
    "    xlabs+=[r'$|'+mybin(i,nqubits)+r'\\rangle$']\n",
    "    xlabs0+['']\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's study the IBM Q Tokyo response matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x138818d30>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nqbits = 5\n",
    "bin_convert = {}\n",
    "for i in range(2**nqbits):\n",
    "  bin_convert[bin(i).split('b')[1].zfill(nqbits)]=i\n",
    "  pass\n",
    "\n",
    "measured = np.zeros(2**nqbits)\n",
    "truth = np.zeros(2**nqbits)\n",
    "response = np.zeros((2**nqbits,2**nqbits))\n",
    "\n",
    "for i in range(2**nqbits):\n",
    "  myfile = open(\"output_gLR0_Tokyo_Unfold.dat\")\n",
    "  for line in myfile:\n",
    "    if \"Unfold_1\" in line:\n",
    "      myline = line.split(\"}\")[i].split(\",\")\n",
    "      for j in myline:\n",
    "        if \":\" not in j:\n",
    "          continue\n",
    "        outbin = 0\n",
    "        counts = 0\n",
    "        if \"{\" in j:\n",
    "          outbin = int(bin_convert[j.split(\"{\")[len(j.split(\"{\"))-1].split(\":\")[0].split(\"'\")[1]])\n",
    "          counts = int(j.split(\"{\")[len(j.split(\"{\"))-1].split(\":\")[1])\n",
    "        else:\n",
    "          outbin = int(bin_convert[j.split(\":\")[0].split(\"'\")[1]])\n",
    "          counts = int(j.split(\":\")[1])\n",
    "          pass\n",
    "        truth[i] += counts\n",
    "        measured[outbin] += counts\n",
    "        response[outbin,i] += counts\n",
    "        pass\n",
    "      pass\n",
    "    pass\n",
    "  pass\n",
    "\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "plt.bar(range(2**nqbits),truth,alpha=0.3,color='b',label='Truth')\n",
    "plt.bar(range(2**nqbits),measured,alpha=0.3,color='r',label='Measured')\n",
    "xvals = []\n",
    "xlabs = []\n",
    "for i in range(2**nqbits):\n",
    "  xvals+=[i+0.5]\n",
    "  xlabs+=[r'$|'+mybin(i,nqbits)+r'\\rangle$']\n",
    "  pass\n",
    "plt.xticks(xvals,xlabs,rotation='vertical',size=10)\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Value\")\n",
    "plt.legend(frameon=False,loc='upper right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "response_normalized = np.zeros((2**nqbits,2**nqbits))\n",
    "response_normalized_reversed = np.zeros((2**nqbits,2**nqbits))\n",
    "response_labels = np.zeros((2**nqbits,2**nqbits))\n",
    "for i in range(2**nqbits):\n",
    "  mysum = 0.\n",
    "  for j in range(2**nqbits):\n",
    "    mysum+=response[j,i]\n",
    "    pass\n",
    "  for j in range(2**nqbits):\n",
    "    response_labels[j][i] = '0'\n",
    "    if (mysum > 0):\n",
    "      response_normalized[j,i] = 100.*response[j,i]/mysum\n",
    "      response_normalized_reversed[i,j] = response_normalized[j,i]\n",
    "      response_labels[j][i] = '%1.0f' % (response_normalized[j,i])\n",
    "      pass\n",
    "    pass\n",
    "  pass\n",
    "\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "sns.set(font_scale=1.5)\n",
    "ax = sns.heatmap(response_normalized,annot=response_labels,annot_kws={\"size\": 8},cmap=\"Blues\")\n",
    "ax.figure.axes[-1].yaxis.label.set_size(18)\n",
    "cbar = ax.collections[0].colorbar\n",
    "cbar.set_label('Pr(Measured | True) [%]', labelpad=30)\n",
    "plt.xticks(xvals,xlabs,rotation='vertical',fontsize=10)\n",
    "plt.yticks(xvals,xlabs,rotation='horizontal',fontsize=10)\n",
    "plt.xlabel('True',fontsize=18)\n",
    "plt.ylabel('Measured',fontsize=18)\n",
    "plt.subplots_adjust(bottom=0.18,left=0.18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "diagonal = [response_normalized[i][i] for i in range(2**5)]\n",
    "numb0s = [xlabs[i].count(\"0\") for i in range(2**5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Pr(measure correct string)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(diagonal)\n",
    "plt.ylabel(\"Counts\")\n",
    "plt.xlabel(\"Pr(measure correct string)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 2b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Pr(measure correct string)')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "plt.scatter(numb0s,diagonal)\n",
    "plt.xlabel(\"Number of 0's in bitstring\")\n",
    "plt.ylabel(\"Pr(measure correct string)\")\n",
    "#plt.savefig(\"scalingwith1sinstrings.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement the IBM Q Tokyo readout errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "nqubits_r = 5\n",
    "\n",
    "nqubitarray_r = []\n",
    "for i in range(nqubits_r):\n",
    "    nqubitarray_r+=[i]\n",
    "    pass \n",
    "\n",
    "mynoise_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    hold = []\n",
    "    for j in range(2**nqubits_r):\n",
    "        #if (i==j):\n",
    "        #    hold+=[1.]\n",
    "        #else:\n",
    "        #    hold+=[0.]\n",
    "        hold+=[response_normalized[j][i]/100.]\n",
    "        pass\n",
    "    mynoise_r+=[hold]\n",
    "    pass\n",
    "c_r = ReadoutError(mynoise_r)\n",
    "noise_model_r = noise.NoiseModel()\n",
    "noise_model_r.add_readout_error(c_r,nqubitarray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#This is a special measure function that allows us to simultaneously measure multiple qubits at the same time.\n",
    "measure2_r = Instruction(\"measure\", nqubits_r, nqubits_r, [])\n",
    "\n",
    "n = 100000\n",
    "\n",
    "simulator_r = Aer.get_backend('qasm_simulator')\n",
    "\n",
    "#Calibration circuit\n",
    "meas_cals_mine_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r)\n",
    "    qr_r = QuantumRegister(nqubits_r, 'qr')\n",
    "    cr_r = ClassicalRegister(nqubits_r, 'cr')\n",
    "    circ_r = QuantumCircuit(qr_r, cr_r)\n",
    "    for j in range(nqubits_r):\n",
    "        if (binnumber_initial[j]=='1'):\n",
    "            circ_r.x(qr_r[j])\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "    circ_r.name='cal_'+binnumber_initial[::-1] #Their convention is backwards of mine.\n",
    "    circ_r.name=circ_r.name\n",
    "    circ_r.append(measure2_r, nqubitarray_r, nqubitarray_r)\n",
    "    meas_cals_mine_r+=[circ_r]\n",
    "    pass\n",
    "\n",
    "#Vector for state labels.\n",
    "state_labels_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    state_labels_r += [mybin(i,nqubits_r)]\n",
    "    pass\n",
    "\n",
    "#These are our custom calibration circuits.\n",
    "job_r = execute(meas_cals_mine_r, backend=simulator_r, shots=int(n/(2**nqubits_r)), noise_model=noise_model_r)\n",
    "cal_results_r = job_r.result()\n",
    "meas_fitter_r = CompleteMeasFitter(cal_results_r, state_labels_r)\n",
    "meas_filter_r = meas_fitter_r.filter\n",
    "\n",
    "\n",
    "#Let's first quickly process these\n",
    "many_counts_r = []\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r)\n",
    "    counts_r = cal_results_r.get_counts(meas_cals_mine_r[i])\n",
    "    many_counts_r+=[counts_r]\n",
    "    pass\n",
    "\n",
    "#Let's make our own matrix based on cal_results.\n",
    "measured_cal_r = np.zeros(2**nqubits_r)\n",
    "truth_cal_r = np.zeros(2**nqubits_r)\n",
    "response_cal_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "for j in range(len(many_counts_r)):\n",
    "    counts = many_counts_r[j]\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial = mybin(i,nqubits_r,0)\n",
    "        if (binnumber_initial in counts):\n",
    "            measured_cal_r[i]+=counts[binnumber_initial]\n",
    "            truth_cal_r[j]+=counts[binnumber_initial]\n",
    "            response_cal_r[i,j]+=counts[binnumber_initial]\n",
    "            pass\n",
    "        pass\n",
    "    pass\n",
    "response_normalized_cal_r = np.zeros((2**nqubits_r,2**nqubits_r))\n",
    "for i in range(len(truth_cal_r)):\n",
    "    for j in range(len(truth_cal_r)):\n",
    "        response_normalized_cal_r[i,j]=response_cal_r[i,j]/truth_cal_r[j]\n",
    "        pass\n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1: W state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x13a14d6d0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Implementation from https://github.com/qiskit-community/qiskit-community-tutorials/blob/master/awards/teach_me_qiskit_2018/w_state/W%20State%201%20-%20Multi-Qubit%20Systems.ipynb\n",
    "\n",
    "# Here, two useful routine\n",
    "# Define a F_gate\n",
    "def F_gate(circ,q,i,j,n,k) :\n",
    "    theta = np.arccos(np.sqrt(1/(n-k+1)))\n",
    "    circ.ry(-theta,q[j])       \n",
    "    circ.cz(q[i],q[j])\n",
    "    circ.ry(theta,q[j])\n",
    "    circ.barrier(q[i])\n",
    "# Define the cxrv gate which uses reverse CNOT instead of CNOT\n",
    "def  cxrv(circ,q,i,j) :\n",
    "    circ.h(q[i])\n",
    "    circ.h(q[j])\n",
    "    circ.cx(q[j],q[i])\n",
    "    circ.h(q[i])\n",
    "    circ.h(q[j])\n",
    "    circ.barrier(q[i],q[j])\n",
    "    \n",
    "# 5-qubit W state\n",
    "nq = 5\n",
    "q = QuantumRegister(nq) \n",
    "c = ClassicalRegister(nq)\n",
    "W_states = QuantumCircuit(q,c) \n",
    "\n",
    "W_states.x(q[4]) #start is |10000>\n",
    "F_gate(W_states,q,4,3,5,1) # Applying F12\n",
    "F_gate(W_states,q,3,2,5,2) # Applying F23\n",
    "F_gate(W_states,q,2,1,5,3) # Applying F34\n",
    "F_gate(W_states,q,1,0,5,4) # Applying F45\n",
    "W_states.cx(q[3],q[4]) # cNOT 21\n",
    "cxrv(W_states,q,2,3)   # cNOT 32\n",
    "W_states.cx(q[1],q[2]) # cNOT 43\n",
    "W_states.cx(q[0],q[1]) # cNOT 54 \n",
    "for i in range(5) : #Inverted W state\n",
    "    W_states.x([q[i]])\n",
    "W_states.append(measure2_r, nqubitarray_r, nqubitarray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x13a137e20>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W_states_nominal = QuantumCircuit(q,c) \n",
    "\n",
    "W_states_nominal.x(q[4]) #start is |10000>\n",
    "F_gate(W_states_nominal,q,4,3,5,1) # Applying F12\n",
    "F_gate(W_states_nominal,q,3,2,5,2) # Applying F23\n",
    "F_gate(W_states_nominal,q,2,1,5,3) # Applying F34\n",
    "F_gate(W_states_nominal,q,1,0,5,4) # Applying F45\n",
    "W_states_nominal.cx(q[3],q[4]) # cNOT 21\n",
    "cxrv(W_states_nominal,q,2,3)   # cNOT 32\n",
    "W_states_nominal.cx(q[1],q[2]) # cNOT 43\n",
    "W_states_nominal.cx(q[0],q[1]) # cNOT 54 \n",
    "W_states_nominal.append(measure2_r, nqubitarray_r, nqubitarray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, for the actual measurement\n",
    "\n",
    "counts_true = execute(W_states, simulator_r, shots=n).result().get_counts()\n",
    "counts_test = execute(W_states, simulator_r, shots=n, noise_model=noise_model_r).result().get_counts()\n",
    "\n",
    "#Making the truth_test_r and measured_test_r here.\n",
    "truth_test_r = np.zeros(2**nqubits_r)\n",
    "measured_test_r = np.zeros(2**nqubits_r)\n",
    "\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r,0)\n",
    "    truth_test_r[i] = 0\n",
    "    measured_test_r[i] = 0\n",
    "    if (binnumber_initial in counts_true):\n",
    "        truth_test_r[i]+=counts_true[binnumber_initial]\n",
    "    if (binnumber_initial in counts_test):\n",
    "        measured_test_r[i]+=counts_test[binnumber_initial]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, for the unfolding.\n",
    "unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig. 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def myre(x):\n",
    "    return np.concatenate([[x[0]],x])\n",
    "\n",
    "bincenters_r = np.array(xvals)+0.5\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "mybincenters = np.linspace(-0.5,31.5,33)\n",
    "plt.step(mybincenters,myre(truth_test_r),color='black',label=r\"t\")\n",
    "plt.errorbar(0.5*(mybincenters[1:]+mybincenters[:-1]),measured_test_r,yerr=np.sqrt(measured_test_r),label='m',marker='o',linestyle='none',color='black')\n",
    "plt.step(mybincenters,myre(unfolded_IBU_r),color='green',linestyle='-.',label=r\"$\\hat{t}$\")\n",
    "\n",
    "_=plt.xticks(xvals,xlabs0,rotation='vertical',fontsize=10)\n",
    "plt.ylabel('Counts')\n",
    "plt.ylim([0,2.*max(truth_test_r)])\n",
    "plt.legend(loc='upper left',fontsize=20,frameon=False)\n",
    "_=plt.text(20., max(truth_test_r)*1.8, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(20., max(truth_test_r)*1.7, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "_=plt.text(20., max(truth_test_r)*1.6, \"Inverted $W$ state\", fontsize=12)\n",
    "\n",
    "ax2 = plt.subplot(gs[1])\n",
    "ratio_ignis2_r = np.zeros(2**nqubits_r)\n",
    "ratio_ignis_r = np.zeros(2**nqubits_r)\n",
    "ratio_measured_r = np.zeros(2**nqubits_r)\n",
    "ratio_unfold_r = np.zeros(2**nqubits_r)\n",
    "for i in range(len(ratio_unfold_r)):\n",
    "    ratio_unfold_r[i]=1.\n",
    "    if (truth_test_r[i] > 0):\n",
    "        ratio_unfold_r[i] = unfolded_IBU_r[i]/truth_test_r[i]\n",
    "        pass\n",
    "    if (ratio_measured_r[i] > 0):\n",
    "        ratio_measured_r[i] = measured_test_r[i]/truth_test_r[i]\n",
    "    pass\n",
    "\n",
    "plt.ylim([0.8,1.2])\n",
    "plt.step(mybincenters,myre(ratio_unfold_r),color='green')\n",
    "#plt.step(mybincenters,myre(ratio_measured_r),color='black')\n",
    "plt.ylabel('Extracted / Truth')\n",
    "xx2 = [1.,1.]\n",
    "plt.plot([xvals[0],xvals[len(xvals)-1]],xx2,color='black',linestyle=':')\n",
    "_=plt.xticks(xvals,xlabs,rotation='vertical',fontsize=12)\n",
    "#plt.savefig(\"InvertedWState.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Readout Rebalancing Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebalance_method1(input_circuit,nominal_result,nq):\n",
    "    # This method minimizes the average number of 1's in the output.\n",
    "    # input_circuit is the input circuit\n",
    "    # nominal_result is a (small) sample of the result that has been unfolded to get a sense for the balance of 0's and 1's.\n",
    "    # nq is the number of qubits (could also just extract this from the circuit).    \n",
    "    \n",
    "    #CURRENTLY ONLY WORKS FOR nq=5.  Need to make the set of for loops below recursive to get the general case.\n",
    "    \n",
    "    xlabs_hold = []\n",
    "    for i in range(2**nq):\n",
    "        xlabs_hold+=[r'$|'+mybin(i,nq)+r'\\rangle$']\n",
    "        pass    \n",
    "    \n",
    "    mymin = 2**nq+1\n",
    "    swaps = []\n",
    "    for q0 in [0,1]:\n",
    "        for q1 in [0,1]:\n",
    "            for q2 in [0,1]:\n",
    "                for q3 in [0,1]:\n",
    "                    for q4 in [0,1]:\n",
    "                        xlabs_hold = xlabs.copy()\n",
    "                        for j in range(len(xlabs_hold)):\n",
    "                            xlabs_hold[j] = list(xlabs_hold[j])\n",
    "                            if q0==1:\n",
    "                                xlabs_hold[j][2]=str(1-int(xlabs_hold[j][2]))\n",
    "                            if q1==1:\n",
    "                                xlabs_hold[j][3]=str(1-int(xlabs_hold[j][3]))\n",
    "                            if q2==1:\n",
    "                                xlabs_hold[j][4]=str(1-int(xlabs_hold[j][4]))\n",
    "                            if q3==1:\n",
    "                                xlabs_hold[j][5]=str(1-int(xlabs_hold[j][5]))\n",
    "                            if q4==1:\n",
    "                                xlabs_hold[j][6]=str(1-int(xlabs_hold[j][6]))\n",
    "                            xlabs_hold[j] = \"\".join(xlabs_hold[j])\n",
    "                            pass\n",
    "                        numb1s_hold = [xlabs_hold[i].count(\"1\") for i in range(2**nq)]\n",
    "                        val = sum(np.array(nominal_result)*np.array(numb1s_hold))/sum(nominal_result)\n",
    "                        if (val < mymin):\n",
    "                            mymin = val\n",
    "                            swaps = [q0,q1,q2,q3,q4]\n",
    "\n",
    "    input_circuit_modified = input_circuit.copy()\n",
    "    input_circuit_modified.data.pop(len(input_circuit_modified)-1) #remove the measure so we can add x gates\n",
    "    for i in range(len(swaps)):\n",
    "        if (swaps[i]==1):\n",
    "            input_circuit_modified.x(i) \n",
    "    input_circuit_modified.append(measure2_r, nqubitarray_r, nqubitarray_r) #add back the measure \n",
    "    return input_circuit_modified,swaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rebalance_method2(input_circuit,nominal_result,nq):\n",
    "    # This method swaps a qubit if it is on average mostly in the 1 state.\n",
    "    # input_circuit is the input circuit\n",
    "    # nominal_result is a (small) sample of the result that has been unfolded to get a sense for the balance of 0's and 1's.\n",
    "    # nq is the number of qubits (could also just extract this from the circuit).\n",
    "    \n",
    "    input_circuit_modified = input_circuit.copy()\n",
    "    input_circuit_modified.data.pop(len(input_circuit_modified)-1) #remove the measure so we can add x gates\n",
    "\n",
    "    xlabs_hold = []\n",
    "    for i in range(2**nq):\n",
    "        xlabs_hold+=[r'$|'+mybin(i,nq)+r'\\rangle$']\n",
    "        pass\n",
    "    \n",
    "    swaps = []\n",
    "    for i in range(nq):\n",
    "        myavg = 0.\n",
    "        myavg_den = 0.\n",
    "        for j in range(len(xlabs_hold)):\n",
    "            myavg += nominal_result[j]*int(xlabs_hold[j][i+2])\n",
    "            myavg_den+=nominal_result[j]\n",
    "        if (myavg/myavg_den > 0.5): #this qubit is usually 1.\n",
    "            input_circuit_modified.x(i) \n",
    "            swaps+=[1]\n",
    "        else:\n",
    "            swaps+=[0]\n",
    "    input_circuit_modified.append(measure2_r, nqubitarray_r, nqubitarray_r) #add back the measure\n",
    "    return input_circuit_modified,swaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A statistic to check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_stat_uncert(input_circuit,swapmap,N=1000,nshots=100000):\n",
    "\n",
    "    mymapping = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping[mybin(i,nqubits_r,0)] = i\n",
    "    \n",
    "    mymapping_inverse_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping_inverse_r[bin(i).split('b')[1].zfill(nq) ] = i\n",
    "        pass\n",
    "    \n",
    "    hold = []\n",
    "    for k in range(N):\n",
    "\n",
    "        if (k%10==0):\n",
    "            print(\"on i=\",k)\n",
    "\n",
    "        counts_test = execute(input_circuit, simulator_r, shots=nshots, noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test):\n",
    "                measured_test_r[i]+=counts_test[binnumber_initial]\n",
    "        unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_postswap = unfolded_IBU_r.copy()\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r[i]\n",
    "            pass\n",
    "        \n",
    "        #Now, let's compute the average value.\n",
    "        myval = 0.\n",
    "        myval_den = 0.\n",
    "        for i in range(2**nqubits_r):\n",
    "            myval+=unfolded_IBU_r_postswap[i]*mymapping_inverse_r[mybin(i,nqubits_r,0)]\n",
    "            myval_den+=unfolded_IBU_r_postswap[i]\n",
    "        #print(myval/myval_den)\n",
    "        hold+=[myval/myval_den]\n",
    "        pass\n",
    "    return np.mean(hold),np.std(hold),hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_stat_uncert_Google(input_circuit,N=1000,nshots=100000):\n",
    "\n",
    "    #First, construct the fully inverted circuit.\n",
    "    \n",
    "    input_circuit_inverted = input_circuit.copy()\n",
    "    input_circuit_inverted.data.pop(len(input_circuit_inverted)-1) #remove the measure so we can add x gates\n",
    "    for i in range(nqubits_r):\n",
    "        input_circuit_inverted.x(i) \n",
    "    input_circuit_inverted.append(measure2_r, nqubitarray_r, nqubitarray_r) #add back the measure\n",
    "    \n",
    "    mymapping = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping[mybin(i,nqubits_r,0)] = i\n",
    "    \n",
    "    mymapping_inverse_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping_inverse_r[bin(i).split('b')[1].zfill(nq) ] = i\n",
    "        pass\n",
    "    \n",
    "    hold = []\n",
    "    for k in range(N):\n",
    "\n",
    "        if (k%10==0):\n",
    "            print(\"on i=\",k)\n",
    "\n",
    "        #First, the nominal circuit:\n",
    "        counts_test = execute(input_circuit, simulator_r, shots=int(nshots/2), noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test):\n",
    "                measured_test_r[i]+=counts_test[binnumber_initial]\n",
    "        unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_postswap = unfolded_IBU_r.copy()\n",
    "        swapmap=np.zeros(nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r[i]\n",
    "            pass\n",
    "        \n",
    "        #Now, the inverted one:\n",
    "        counts_test_inverted = execute(input_circuit_inverted, simulator_r, shots=int(nshots/2), noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test_inverted):\n",
    "                measured_test_r[i]+=counts_test_inverted[binnumber_initial]\n",
    "        unfolded_IBU_r_inverted = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_inverted_postswap = unfolded_IBU_r_inverted.copy()\n",
    "        swapmap=np.ones(nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_inverted_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r_inverted[i]\n",
    "            pass\n",
    "        \n",
    "        #Now, let's compute the average value.\n",
    "        myval = 0.\n",
    "        myval_den = 0.\n",
    "        for i in range(2**nqubits_r):\n",
    "            myval+=(unfolded_IBU_r_postswap[i]+unfolded_IBU_r_inverted_postswap[i])*mymapping_inverse_r[mybin(i,nqubits_r,0)]\n",
    "            myval_den+=unfolded_IBU_r_postswap[i]+unfolded_IBU_r_inverted_postswap[i]\n",
    "        #print(myval/myval_den)\n",
    "        hold+=[myval/myval_den]\n",
    "        pass\n",
    "    return np.mean(hold),np.std(hold),hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1] [1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "W_states_modified1,swapmap1 = rebalance_method1(W_states,unfolded_IBU_r,5)\n",
    "W_states_modified2,swapmap2 = rebalance_method2(W_states,unfolded_IBU_r,5)\n",
    "print(swapmap1,swapmap2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "rerun = False\n",
    "resave = False\n",
    "\n",
    "if (rerun):\n",
    "    mWg,sWg,hWg = mean_stat_uncert_Google(W_states)\n",
    "    mWn,sWn,hWn = mean_stat_uncert(W_states,[0,0,0,0,0])\n",
    "    mW,sW,hW = mean_stat_uncert(W_states_modified2,swapmap2)\n",
    "    if (resave):\n",
    "        np.save(\"files/WstateMeansStds\",np.array([mWg,sWg,mWn,sWn,mW,sW]))\n",
    "        np.save(\"files/WstateGoogle\",np.array(hWg))\n",
    "        np.save(\"files/WstateNominal\",np.array(hWn))\n",
    "        np.save(\"files/WstateRRB\",np.array(hW))\n",
    "else:\n",
    "    mWg,sWg,mWn,sWn,mW,sW = np.load(\"files/WstateMeansStds.npy\")\n",
    "    hWg = np.load(\"files/WstateGoogle.npy\")\n",
    "    hWn = np.load(\"files/WstateNominal.npy\")\n",
    "    hW = np.load(\"files/WstateRRB.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_stat(in_vals):\n",
    "    mystore = []\n",
    "    for i in range(1000):\n",
    "        hold = []\n",
    "        for j in range(len(in_vals)):\n",
    "            for k in range(np.random.poisson(1)):\n",
    "                hold+=[in_vals[j]]\n",
    "                pass\n",
    "            pass\n",
    "        mystore+=[np.mean(hold)]\n",
    "        pass\n",
    "    return(np.std(mystore)) #should be the same as std/\\sqrt{n}\n",
    "\n",
    "def std_stat(in_vals):\n",
    "    mystore = []\n",
    "    for i in range(1000):\n",
    "        hold = []\n",
    "        for j in range(len(in_vals)):\n",
    "            for k in range(np.random.poisson(1)):\n",
    "                hold+=[in_vals[j]]\n",
    "                pass\n",
    "            pass\n",
    "        mystore+=[np.std(hold)]\n",
    "        pass\n",
    "    return(np.std(mystore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.74725613272605 +/- 0.0006822316906035279\n",
      "24.704741612746297 +/- 0.000700545034513245\n",
      "24.794277639791606 +/- 0.0005842574948458055\n",
      "\n",
      "\n",
      "0.021430310670331347 +/- 0.0005080999622971401\n",
      "0.02319365580168688 +/- 0.0005170676756929435\n",
      "0.018883629593656113 +/- 0.0003952355145799232\n"
     ]
    }
   ],
   "source": [
    "print(mWg,\"+/-\",mean_stat(hWg)) #should be same as sWg*1000**-0.5\n",
    "print(mWn,\"+/-\",mean_stat(hWn)) #should be same as sWn*1000**-0.5\n",
    "print(mW,\"+/-\",mean_stat(hW)) #should be same as sW*1000**-0.5\n",
    "print(\"\\n\")\n",
    "print(sWg,\"+/-\",std_stat(hWg))\n",
    "print(sWn,\"+/-\",std_stat(hWn))\n",
    "print(sW,\"+/-\",std_stat(hW))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2: Grover search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.aqua.algorithms import Grover\n",
    "from qiskit.aqua.components.oracles import LogicalExpressionOracle\n",
    "from qiskit import Aer\n",
    "from qiskit.visualization import plot_histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "expression = LogicalExpressionOracle(\"a & b & c & d & e\") #Gives us a state of 11111 for our result.\n",
    "expression.construct_circuit()\n",
    "G = Grover(expression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x139d7c5b0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grover_nominal = Grover.construct_circuit(G, measurement = False)\n",
    "cr = ClassicalRegister(5)\n",
    "grover_nominal.add_register(cr)\n",
    "grover_nominal.append(measure2_r, [0,1,2,3,4],[0,1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, for the actual measurement\n",
    "\n",
    "counts_true = execute(grover_nominal, simulator_r, shots=n).result().get_counts()\n",
    "counts_test = execute(grover_nominal, simulator_r, shots=n, noise_model=noise_model_r).result().get_counts()\n",
    "\n",
    "#Making the truth_test_r and measured_test_r here.\n",
    "truth_test_r = np.zeros(2**nqubits_r)\n",
    "measured_test_r = np.zeros(2**nqubits_r)\n",
    "\n",
    "for i in range(2**nqubits_r):\n",
    "    binnumber_initial = mybin(i,nqubits_r,0)\n",
    "    truth_test_r[i] = 0\n",
    "    measured_test_r[i] = 0\n",
    "    if (binnumber_initial in counts_true):\n",
    "        truth_test_r[i]+=counts_true[binnumber_initial]\n",
    "    if (binnumber_initial in counts_test):\n",
    "        measured_test_r[i]+=counts_test[binnumber_initial]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now, for the unfolding.\n",
    "unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def myre(x):\n",
    "    return np.concatenate([[x[0]],x])\n",
    "\n",
    "bincenters_r = np.array(xvals)+0.5\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(2, 1, width_ratios=[1], height_ratios=[3.5, 1])\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "mybincenters = np.linspace(-0.5,31.5,33)\n",
    "plt.step(mybincenters,myre(truth_test_r),color='black',label=r\"t\")\n",
    "plt.errorbar(0.5*(mybincenters[1:]+mybincenters[:-1]),measured_test_r,yerr=np.sqrt(measured_test_r),label='m',marker='o',linestyle='none',color='black')\n",
    "plt.step(mybincenters,myre(unfolded_IBU_r),color='green',linestyle='-.',label=r\"$\\hat{t}$\")\n",
    "\n",
    "_=plt.xticks(xvals,xlabs0,rotation='vertical',fontsize=10)\n",
    "plt.ylabel('Counts')\n",
    "plt.ylim([0,2.*max(truth_test_r)])\n",
    "plt.legend(loc='upper left',fontsize=20,frameon=False)\n",
    "_=plt.text(20., max(truth_test_r)*1.8, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(20., max(truth_test_r)*1.7, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "_=plt.text(20., max(truth_test_r)*1.6, \"Grover with 11111 oracle\", fontsize=12)\n",
    "\n",
    "ax2 = plt.subplot(gs[1])\n",
    "ratio_ignis2_r = np.zeros(2**nqubits_r)\n",
    "ratio_ignis_r = np.zeros(2**nqubits_r)\n",
    "ratio_measured_r = np.zeros(2**nqubits_r)\n",
    "ratio_unfold_r = np.zeros(2**nqubits_r)\n",
    "for i in range(len(ratio_unfold_r)):\n",
    "    ratio_unfold_r[i]=1.\n",
    "    if (truth_test_r[i] > 0):\n",
    "        ratio_unfold_r[i] = unfolded_IBU_r[i]/truth_test_r[i]\n",
    "        pass\n",
    "    if (ratio_measured_r[i] > 0):\n",
    "        ratio_measured_r[i] = measured_test_r[i]/truth_test_r[i]\n",
    "    pass\n",
    "\n",
    "plt.ylim([0.8,1.2])\n",
    "plt.step(mybincenters,myre(ratio_unfold_r),color='green')\n",
    "#plt.step(mybincenters,myre(ratio_measured_r),color='black')\n",
    "plt.ylabel('Extracted / Truth')\n",
    "xx2 = [1.,1.]\n",
    "plt.plot([xvals[0],xvals[len(xvals)-1]],xx2,color='black',linestyle=':')\n",
    "_=plt.xticks(xvals,xlabs,rotation='vertical',fontsize=12)\n",
    "plt.savefig(\"GroverState.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 1, 1] [1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "Grover_modified1,swapmap1 = rebalance_method1(grover_nominal,unfolded_IBU_r,5)\n",
    "Grover_modified2,swapmap2 = rebalance_method2(grover_nominal,unfolded_IBU_r,5)\n",
    "print(swapmap1,swapmap2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highval_stat_uncert(input_circuit,swapmap,N=1000,nshots=100000):\n",
    "\n",
    "    mymapping = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping[mybin(i,nqubits_r,0)] = i\n",
    "    \n",
    "    mymapping_inverse_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping_inverse_r[bin(i).split('b')[1].zfill(nq) ] = i\n",
    "        pass\n",
    "    \n",
    "    hold = []\n",
    "    for k in range(N):\n",
    "\n",
    "        if (k%10==0):\n",
    "            print(\"on i=\",k)\n",
    "\n",
    "        counts_test = execute(input_circuit, simulator_r, shots=nshots, noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test):\n",
    "                measured_test_r[i]+=counts_test[binnumber_initial]\n",
    "        unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_postswap = unfolded_IBU_r.copy()\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r[i]\n",
    "            pass\n",
    "        \n",
    "        #Now, let's compute the average value.\n",
    "        myval = 0.\n",
    "        for i in range(2**nqubits_r):\n",
    "            print(i,unfolded_IBU_r_postswap[i])\n",
    "            if (i!=2**nqubits_r-1):\n",
    "                continue\n",
    "            myval+=unfolded_IBU_r_postswap[i]\n",
    "        hold+=[myval]\n",
    "        pass\n",
    "    return np.mean(hold),np.std(hold),hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def highval_stat_uncert_Google(input_circuit,N=1000,nshots=100000):\n",
    "\n",
    "    #First, construct the fully inverted circuit.\n",
    "    \n",
    "    input_circuit_inverted = input_circuit.copy()\n",
    "    input_circuit_inverted.data.pop(len(input_circuit_inverted)-1) #remove the measure so we can add x gates\n",
    "    for i in range(nqubits_r):\n",
    "        input_circuit_inverted.x(i) \n",
    "    input_circuit_inverted.append(measure2_r, nqubitarray_r, nqubitarray_r) #add back the measure\n",
    "    \n",
    "    mymapping = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping[mybin(i,nqubits_r,0)] = i\n",
    "    \n",
    "    mymapping_inverse_r = {}\n",
    "    for i in range(2**nqubits_r):\n",
    "        mymapping_inverse_r[bin(i).split('b')[1].zfill(nq) ] = i\n",
    "        pass\n",
    "    \n",
    "    hold = []\n",
    "    for k in range(N):\n",
    "\n",
    "        if (k%10==0):\n",
    "            print(\"on i=\",k)\n",
    "\n",
    "        #First, the nominal circuit:\n",
    "        counts_test = execute(input_circuit, simulator_r, shots=int(nshots/2), noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test):\n",
    "                measured_test_r[i]+=counts_test[binnumber_initial]\n",
    "        unfolded_IBU_r = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_postswap = unfolded_IBU_r.copy()\n",
    "        swapmap=np.zeros(nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r[i]\n",
    "            pass\n",
    "        \n",
    "        #Now, the inverted one:\n",
    "        counts_test_inverted = execute(input_circuit_inverted, simulator_r, shots=int(nshots/2), noise_model=noise_model_r).result().get_counts()\n",
    "        \n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_r[i] = 0\n",
    "            if (binnumber_initial in counts_test_inverted):\n",
    "                measured_test_r[i]+=counts_test_inverted[binnumber_initial]\n",
    "        unfolded_IBU_r_inverted = IBU(measured_test_r,truth_cal_r,response_normalized_cal_r,100) \n",
    "        \n",
    "        unfolded_IBU_r_inverted_postswap = unfolded_IBU_r_inverted.copy()\n",
    "        swapmap=np.ones(nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            #print(binnumber_initial)\n",
    "            binnumber_initial = list(binnumber_initial)\n",
    "            for j in range(nqubits_r):\n",
    "                if (swapmap[j]==1):\n",
    "                    binnumber_initial[-j-1] = str(1-int(binnumber_initial[-j-1])) #the convention here is backwards!\n",
    "            binnumber_initial = \"\".join(binnumber_initial)  \n",
    "            unfolded_IBU_r_inverted_postswap[mymapping[binnumber_initial]] = unfolded_IBU_r_inverted[i]\n",
    "            pass\n",
    "        \n",
    "       #Now, let's compute the average value.\n",
    "        myval = 0.\n",
    "        for i in range(2**nqubits_r):\n",
    "            #print(i,unfolded_IBU_r_postswap[i]+unfolded_IBU_r_inverted_postswap[i])\n",
    "            if (i!=2**nqubits_r-1):\n",
    "                continue\n",
    "            myval+=unfolded_IBU_r_postswap[i]+unfolded_IBU_r_inverted_postswap[i]\n",
    "        hold+=[myval]\n",
    "        pass\n",
    "    return np.mean(hold),np.std(hold),hold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "rerun = False\n",
    "resave = False\n",
    "if (rerun):\n",
    "    m_gg,s_gg,h_gg = highval_stat_uncert_Google(grover_nominal)\n",
    "    m_gn,s_gn,h_gn=highval_stat_uncert(grover_nominal,[0,0,0,0,0])\n",
    "    m_g,s_g,h_g=highval_stat_uncert(Grover_modified2,swapmap2)\n",
    "    if (resave):\n",
    "        np.save(\"files/GroverMeansStds\",np.array([m_gg,s_gg,m_gn,s_gn,m_g,s_g]))\n",
    "        np.save(\"files/GroverGoogle\",np.array(h_gg))\n",
    "        np.save(\"files/GroverNominal\",np.array(h_gn))\n",
    "        np.save(\"files/GroverRRB\",np.array(h_g))\n",
    "else:\n",
    "    m_gg,s_gg,m_gn,s_gn,m_g,s_g = np.load(\"files/GroverMeansStds.npy\")\n",
    "    h_gg = np.load(\"files/GroverGoogle.npy\")\n",
    "    h_gn = np.load(\"files/GroverNominal.npy\")\n",
    "    h_g = np.load(\"files/GroverRRB.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25761.058572624817 +/- 6.150710207461073\n",
      "25715.913667576893 +/- 6.7607072551303515\n",
      "25771.311788890027 +/- 5.010266552477281\n",
      "\n",
      "\n",
      "185.82386699882187 +/- 4.065346326146506\n",
      "210.80846409090367 +/- 4.370187571128279\n",
      "159.96399070934038 +/- 3.5232012264846215\n"
     ]
    }
   ],
   "source": [
    "print(m_gg,\"+/-\",mean_stat(h_gg)) #should be same as s_gg*1000**-0.5\n",
    "print(m_gn,\"+/-\",mean_stat(h_gn)) #should be same as s_gn*1000**-0.5\n",
    "print(m_g,\"+/-\",mean_stat(h_g)) #should be same as s_g*1000**-0.5\n",
    "print(\"\\n\")\n",
    "print(s_gg,\"+/-\",std_stat(h_gg))\n",
    "print(s_gn,\"+/-\",std_stat(h_gn))\n",
    "print(s_g,\"+/-\",std_stat(h_g))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gaussian example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.aqua.components.uncertainty_models import UnivariateVariationalDistribution, NormalDistribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bincenters_r = np.array(xvals)+0.5\n",
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(1, 1)\n",
    "ax1 = plt.subplot(gs[0])\n",
    "ax1.set_xticklabels( () )\n",
    "\n",
    "colors = [\"darkslateblue\",\"darkviolet\",\"violet\",\"deeppink\",\"tomato\",\"darkorange\",\"gold\",\"palegoldenrod\",\"palegreen\",\"forestgreen\",\"mediumturquoise\",\"darkcyan\",\"black\",\"black\",\"black\",\"black\",\"black\"]\n",
    "\n",
    "ii=0\n",
    "for mean in np.linspace(-1,1,10):\n",
    "    Gaussian_state = QuantumCircuit(q,c) \n",
    "    Gaussian_state.initialize(NormalDistribution(5,mu=mean,sigma=0.1,low=-1,high=1).probabilities**0.5, q)\n",
    "    Gaussian_state.append(measure2_r, nqubitarray_r, nqubitarray_r) \n",
    "    result = execute(Gaussian_state, simulator_r, shots=100000).result()\n",
    "    gaussian_counts = result.get_counts()\n",
    "    measured_test_gaussian = np.zeros(2**nqubits_r)\n",
    "    for i in range(2**nqubits_r):\n",
    "        binnumber_initial = mybin(i,nqubits_r,0)\n",
    "        measured_test_gaussian[i] = 0\n",
    "        if (binnumber_initial in gaussian_counts):\n",
    "            measured_test_gaussian[i]+=gaussian_counts[binnumber_initial]\n",
    "\n",
    "    mybincenters = np.linspace(-0.5,31.5,33)\n",
    "    plt.step(mybincenters,myre(measured_test_gaussian),color=colors[ii],label=\"$\\mu=\"+\"%0.2f\" % mean+\"$\")\n",
    "    ii+=1\n",
    "\n",
    "_=plt.xticks(xvals,xlabs0,rotation='vertical',fontsize=10)\n",
    "plt.ylabel('Counts')\n",
    "plt.ylim([0,2.*max(truth_test_r)])\n",
    "plt.legend(bbox_to_anchor = [0.8, 0.5],fontsize=15,frameon=False, ncol=2)\n",
    "_=plt.text(20., 26000*1.8, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(20., 26000*1.7, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "_=plt.text(20., 26000*1.6, \"Gaussian states\", fontsize=12)\n",
    "\n",
    "plt.ylim([0,50000])\n",
    "_=plt.xticks(xvals,xlabs,rotation='vertical',fontsize=12)\n",
    "plt.savefig(\"GaussianState_shift.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sweep Gaussian from right to left\n",
    "\n",
    "rerun = False\n",
    "resave = False\n",
    "\n",
    "if (rerun):\n",
    "\n",
    "    mean_vals2 = []\n",
    "    sigma_vals2 = []\n",
    "    emean_vals2 = []\n",
    "    esigma_vals2 = []\n",
    "    x_vals2 = []\n",
    "    \n",
    "    mean_valsG = []\n",
    "    sigma_valsG = []\n",
    "    emean_valsG = []\n",
    "    esigma_valsG = []\n",
    "    x_valsG = []  \n",
    "    \n",
    "    mean_vals = []\n",
    "    sigma_vals = []\n",
    "    emean_vals = []\n",
    "    esigma_vals = []\n",
    "    x_vals = []    \n",
    "    \n",
    "    for mean in np.linspace(-1,1,10):\n",
    "        Gaussian_state = QuantumCircuit(q,c) \n",
    "        Gaussian_state.initialize(NormalDistribution(5,mu=mean,sigma=0.1,low=-1,high=1).probabilities**0.5, q)\n",
    "        Gaussian_state.append(measure2_r, nqubitarray_r, nqubitarray_r) \n",
    "        result = execute(Gaussian_state, simulator_r, shots=100000).result()\n",
    "        gaussian_counts = result.get_counts()\n",
    "        measured_test_gaussian = np.zeros(2**nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_gaussian[i] = 0\n",
    "            if (binnumber_initial in gaussian_counts):\n",
    "                measured_test_gaussian[i]+=gaussian_counts[binnumber_initial]\n",
    "        #Gaussian_state_modified,swapmap1_gaussian = rebalance_method2(Gaussian_state,measured_test_gaussian,5)\n",
    "        mean_hold,sigma_hold,vals_hold = mean_stat_uncert(Gaussian_state,[0,0,0,0,0],N=1000,nshots=10000)\n",
    "        print(mean,mean_hold,sigma_hold)\n",
    "        x_vals2+=[mean]\n",
    "        mean_vals2+=[mean_hold]\n",
    "        sigma_vals2+=[sigma_hold]\n",
    "        emean_vals2+=[mean_stat(vals_hold)]\n",
    "        esigma_vals2+=[std_stat(vals_hold)]        \n",
    "        \n",
    "        mean_holdG,sigma_holdG,vals_holdG = mean_stat_uncert_Google(Gaussian_state)\n",
    "        x_valsG+=[vals_holdG]\n",
    "        mean_valsG+=[mean_holdG]\n",
    "        sigma_valsG+=[sigma_holdG]            \n",
    "        emean_valsG+=[mean_stat(vals_holdG)]\n",
    "        esigma_valsG+=[std_stat(vals_holdG)]         \n",
    "        \n",
    "    for mean in np.linspace(-1,1,10):\n",
    "        Gaussian_state = QuantumCircuit(q,c) \n",
    "        Gaussian_state.initialize(NormalDistribution(5,mu=mean,sigma=0.1,low=-1,high=1).probabilities**0.5, q)\n",
    "        Gaussian_state.append(measure2_r, nqubitarray_r, nqubitarray_r) \n",
    "        result = execute(Gaussian_state, simulator_r, shots=100000).result()\n",
    "        gaussian_counts = result.get_counts()\n",
    "        measured_test_gaussian = np.zeros(2**nqubits_r)\n",
    "        for i in range(2**nqubits_r):\n",
    "            binnumber_initial = mybin(i,nqubits_r,0)\n",
    "            measured_test_gaussian[i] = 0\n",
    "            if (binnumber_initial in gaussian_counts):\n",
    "                measured_test_gaussian[i]+=gaussian_counts[binnumber_initial]\n",
    "        Gaussian_state_modified,swapmap1_gaussian = rebalance_method2(Gaussian_state,measured_test_gaussian,5)\n",
    "        mean_hold,sigma_hold,vals_hold = mean_stat_uncert(Gaussian_state_modified,swapmap1_gaussian,N=1000,nshots=10000)\n",
    "        print(mean,swapmap1_gaussian,mean_hold,sigma_hold)\n",
    "        x_vals+=[mean]\n",
    "        mean_vals+=[mean_hold]\n",
    "        sigma_vals+=[sigma_hold]  \n",
    "        emean_vals+=[mean_stat(vals_hold)]\n",
    "        esigma_vals+=[std_stat(vals_hold)]  \n",
    "\n",
    "    if (resave):\n",
    "        np.save(\"files/Gaussianmean_vals2\",mean_vals2)\n",
    "        np.save(\"files/Gaussiansigma_vals2\",sigma_vals2)\n",
    "        np.save(\"files/Gaussianemean_vals2\",emean_vals2)\n",
    "        np.save(\"files/Gaussianesigma_vals2\",esigma_vals2)\n",
    "        np.save(\"files/Gaussianx_vals2\",x_vals2)\n",
    "\n",
    "        np.save(\"files/Gaussianmean_valsG\",mean_valsG)\n",
    "        np.save(\"files/Gaussiansigma_valsG\",sigma_valsG)\n",
    "        np.save(\"files/Gaussianemean_valsG\",emean_valsG)\n",
    "        np.save(\"files/Gaussianesigma_valsG\",esigma_valsG)\n",
    "        np.save(\"files/Gaussianx_valsG\",x_valsG)        \n",
    "        \n",
    "        np.save(\"files/Gaussianmean_vals\",mean_vals)\n",
    "        np.save(\"files/Gaussiansigma_vals\",sigma_vals)\n",
    "        np.save(\"files/Gaussianemean_vals\",emean_vals)\n",
    "        np.save(\"files/Gaussianesigma_vals\",esigma_vals)\n",
    "        np.save(\"files/Gaussianx_vals\",x_vals)\n",
    "\n",
    "else:\n",
    "    mean_vals2 = np.load(\"files/Gaussianmean_vals2.npy\")\n",
    "    sigma_vals2 = np.load(\"files/Gaussiansigma_vals2.npy\")\n",
    "    emean_vals2 = np.load(\"files/Gaussianemean_vals2.npy\")\n",
    "    esigma_vals2 = np.load(\"files/Gaussianesigma_vals2.npy\")\n",
    "    x_vals2 = np.load(\"files/Gaussianx_vals2.npy\")\n",
    "\n",
    "    mean_valsG = np.load(\"files/Gaussianmean_valsG.npy\")\n",
    "    sigma_valsG = np.load(\"files/Gaussiansigma_valsG.npy\")\n",
    "    emean_valsG = np.load(\"files/Gaussianemean_valsG.npy\")\n",
    "    esigma_valsG = np.load(\"files/Gaussianesigma_valsG.npy\")\n",
    "    x_valsG = np.load(\"files/Gaussianx_valsG.npy\")    \n",
    "    \n",
    "    mean_vals = np.load(\"files/Gaussianmean_vals.npy\")\n",
    "    sigma_vals = np.load(\"files/Gaussiansigma_vals.npy\")\n",
    "    emean_vals = np.load(\"files/Gaussianemean_vals.npy\")\n",
    "    esigma_vals = np.load(\"files/Gaussianesigma_vals.npy\")\n",
    "    x_vals = np.load(\"files/Gaussianx_vals.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(1, 1)\n",
    "\n",
    "plt.errorbar(x_vals,sigma_vals,yerr=esigma_vals,label=\"Readout Rebalancing\",marker='o',fillstyle='none')\n",
    "plt.errorbar(x_vals,sigma_valsG,yerr=esigma_valsG,label=\"Symmetrized Readout\",marker='^',fillstyle='none')\n",
    "plt.errorbar(x_vals,sigma_vals2,yerr=esigma_vals2,label=\"Default\",marker='s',fillstyle='none')\n",
    "\n",
    "plt.legend(frameon=False,loc=\"lower center\")\n",
    "\n",
    "_=plt.text(-1, 0.0155, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(-1, 0.015, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "_=plt.text(-1, 0.0145, \"Gaussian states\", fontsize=12)\n",
    "\n",
    "plt.xlabel(\"Gaussian Mean\")\n",
    "plt.ylabel(\"Binary Value Standard Deviation\")\n",
    "plt.savefig(\"std_mean.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 612x612 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"white\")\n",
    "f = plt.figure(figsize=(8.5, 8.5))\n",
    "gs = GridSpec(1, 1)\n",
    "\n",
    "plt.errorbar(x_vals,mean_vals,yerr=emean_vals,label=\"Readout Rebalancing\",marker='o',fillstyle='none')\n",
    "plt.errorbar(x_vals,mean_valsG,yerr=emean_valsG,label=\"Symmetrized Readout\",marker='^',fillstyle='none')\n",
    "plt.errorbar(x_vals,mean_vals2,yerr=emean_vals2,label=\"Default\",marker='s',fillstyle='none')\n",
    "\n",
    "plt.legend(frameon=False,loc=\"lower center\")\n",
    "\n",
    "_=plt.text(-1, 30, \"Qiskit Simulator\", fontsize=18)\n",
    "_=plt.text(-1, 28, \"IBMQ Tokyo Readout Errors\", fontsize=12)\n",
    "_=plt.text(-1, 26, \"Gaussian states\", fontsize=12)\n",
    "plt.xlabel(\"Gaussian Mean\")\n",
    "plt.ylabel(\"Binary Value Mean\")\n",
    "plt.savefig(\"mean_mean.pdf\",bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table"
   ]
  }
 ],
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   "display_name": "Python 3",
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   "name": "python3"
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