2qubit_balanced.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5519c1a1-c2c2-4990-9aa3-8efc44d8fb5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/83197244.py:14: DeprecationWarning: The qiskit.ignis package is deprecated and has been supersceded by the qiskit-experiments project. Refer to the migration guide: https://github.com/Qiskit/qiskit-ignis#migration-guide on how to migrate to the new project.\n",
      "  from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n",
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, assemble, transpile, Aer, IBMQ\n",
    "# from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "# from ibm_quantum_widgets import *\n",
    "# from qiskit.providers.aer import QasmSimulator\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit import execute\n",
    "\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "\n",
    "from qiskit.ignis.mitigation.measurement import complete_meas_cal, CompleteMeasFitter\n",
    "from qiskit import QuantumRegister\n",
    "from qiskit.visualization import array_to_latex\n",
    "\n",
    "# Loading your IBM Quantum account(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3486471a-1590-41b7-bc03-b0f85159c134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "backend used:  ibmq_belem\n"
     ]
    }
   ],
   "source": [
    "# Load our saved IBMQ accounts and get the least busy backend device with greater than or equal to (n+1) qubits\n",
    "IBMQ.load_account()\n",
    "provider = IBMQ.get_provider(hub='ibm-q')\n",
    "# provider = IBMQ.get_provider(hub='ibm')\n",
    "real_backend = least_busy(provider.backends(filters=lambda x: not x.configuration().simulator))\n",
    "# real_backend = provider.get_backend('ibm_lagos')\n",
    "print(\"backend used: \", real_backend)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc5d74c7-e488-49ad-b157-2e21ed3f9621",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dj_oracle(case, n):\n",
    "    # We need to make a QuantumCircuit object to return\n",
    "    # This circuit has n+1 qubits: the size of the input,\n",
    "    # plus one output qubit\n",
    "    oracle_qc = QuantumCircuit(n+1)\n",
    "    \n",
    "    # First, let's deal with the case in which oracle is balanced\n",
    "    if case == \"balanced\":\n",
    "        # First generate a random number that tells us which CNOTs to\n",
    "        # wrap in X-gates:\n",
    "        b = np.random.randint(1,2**n)\n",
    "        # Next, format 'b' as a binary string of length 'n', padded with zeros:\n",
    "        b_str = format(b, '0'+str(n)+'b')\n",
    "        # Next, we place the first X-gates. Each digit in our binary string \n",
    "        # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1\n",
    "        # we apply an X-gate to that qubit:\n",
    "        for qubit in range(len(b_str)):\n",
    "            if b_str[qubit] == '1':\n",
    "                oracle_qc.x(qubit)\n",
    "        # Do the controlled-NOT gates for each qubit, using the output qubit \n",
    "        # as the target:\n",
    "        for qubit in range(n):\n",
    "            oracle_qc.cx(qubit, n)\n",
    "        # Next, place the final X-gates\n",
    "        for qubit in range(len(b_str)):\n",
    "            if b_str[qubit] == '1':\n",
    "                oracle_qc.x(qubit)\n",
    "\n",
    "    # Case in which oracle is constant\n",
    "    if case == \"constant\":\n",
    "        # First decide what the fixed output of the oracle will be\n",
    "        # (either always 0 or always 1)\n",
    "        output = np.random.randint(2)\n",
    "        if output == 1:\n",
    "            oracle_qc.x(n)\n",
    "    oracle_qc.name = \"oracle \" + case\n",
    "    return oracle_qc\n",
    "    # oracle_gate = oracle_qc.to_gate()\n",
    "    # oracle_gate.name = \"Oracle\" # To show when we display the circuit\n",
    "    # return oracle_gate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c524e501-27ad-4db2-bfbb-9968e86e48e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def dj_algorithm(oracle, n):\n",
    "    dj_circuit = QuantumCircuit(n+1, n)\n",
    "    # Set up the output qubit:\n",
    "    dj_circuit.x(n)\n",
    "    dj_circuit.h(n)\n",
    "    # And set up the input register:\n",
    "    for qubit in range(n):\n",
    "        dj_circuit.h(qubit)\n",
    "    # Let's append the oracle gate to our circuit:\n",
    "    # dj_circuit.barrier()\n",
    "    dj_circuit.append(oracle, range(n+1))\n",
    "    # Finally, perform the H-gates again and measure:\n",
    "    # dj_circuit.barrier()\n",
    "    for qubit in range(n):\n",
    "        dj_circuit.h(qubit)\n",
    "    \n",
    "    for i in range(n):\n",
    "        dj_circuit.measure(i, i)\n",
    "    \n",
    "    return dj_circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a46415f0-7d9b-4c84-b43c-0394e442f0bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d83b1245-3c1d-4bfa-aa3e-c55477623c23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 327.397x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oracle_qc = dj_oracle('balanced', n)\n",
    "oracle_qc.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3af5a7e1-932f-4c04-be87-68d6de0f0268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 568.197x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_circuit = dj_algorithm(oracle_qc, n)\n",
    "dj_circuit.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5804b33f-9fc3-4de4-b9f2-f1c3584c138c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 568.197x264.88 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_dj = dj_circuit.decompose('oracle balanced')\n",
    "\n",
    "new_dj.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b6161d0-9199-4f92-8bc4-0619f8067766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aer_backend = Aer.get_backend('aer_simulator')\n",
    "aer_job = execute(dj_circuit, aer_backend, shots=1000)\n",
    "aer_results = aer_job.result()\n",
    "aer_counts = aer_results.get_counts()\n",
    "plot_histogram(aer_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "219f1f7c-1487-49e8-94a6-dc4b50654fc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "job = execute(dj_circuit, real_backend, shots=1000)\n",
    "realqc_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b0ff19bd-eb00-4c87-a4cd-959983c30f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = realqc_results.get_counts()\n",
    "plot_histogram(counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ba89f641-fbf4-4b21-bad5-f28d1d6de5bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# QEM\n",
    "qr = QuantumRegister(n)\n",
    "qem_meas_calibs, state_labels = complete_meas_cal(qr=qr, circlabel='mcal')\n",
    "qem_cal_results = real_backend.run(qem_meas_calibs, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e3ad93f8-98d2-455c-9b46-f3b68b99383e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9748 & 0.0304 & 0.047 & 0.0014  \\\\\n",
       " 0.0132 & 0.9639 & 0.0004 & 0.0463  \\\\\n",
       " 0.0119 & 0.0001 & 0.9415 & 0.0278  \\\\\n",
       " 0.0001 & 0.0056 & 0.0111 & 0.9245  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_fitter = CompleteMeasFitter(qem_cal_results, state_labels, circlabel='mcal')\n",
    "array_to_latex(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7dd56a1-b5c8-47dc-82cd-4379f6d58b5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(realqc_results)\n",
    "mitigated_counts = mitigated_results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5149a5e3-9280-4395-a433-58daa3480d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noisy_counts = realqc_results.get_counts()\n",
    "plot_histogram([noisy_counts, mitigated_counts], legend=['noisy', 'mitigated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a17cc147-635e-4824-814a-d8a5f0ff6de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GEM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9d8c2c73-50ef-4d17-b05c-b483d526ebaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAACoCAYAAADerTQhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUtklEQVR4nO3de1BTd6IH8O9JAggSI/fiowjYooIS5W0vPkpA7m1pvR1di6i1jGVxQLBb7UpXHetud20drbilvXvt6lZqrVN3FxVqH7ZWa9IqrZYKPsYqu0qX4vUJWsyqPJLcPxxZESQJJPmdg9/PzJkJJznnfMGf3zn55SSRbDabDURE5HEq0QGIiO5XLGAiIkFYwEREgrCAiYgEYQETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMRCcICJiIShAVMRCQIC5iISBAWMBGRICxgIiJBWMBERIKwgImIBGEBExEJwgImIhKEBUxEJAgLmIhIEBYwEZEgLGAiIkE0ogPI3Y4K4OwVMcceEgBMS+jetotOncCRa9dcG8gB0Vot1kZEdmtbUZmBnuUWNUZ6Mj4WLlyIqqoql+ZxVExMDIqKioQcW25YwHacvQKcvig6hfOOXLuGL680iI7hFCVmBpQ5RqqqqmAymUTHuO9xCoKISBAWMBGRICxgIiJBWMBERIKwgImIBGEBExEJwsvQiMghfn5+GD16NHQ6HVpbW3H69GnU1tbe8/FxcXEYNGgQdu3a5cGUysICJqJ70ul0mDNnDrKysjBmzBio1ep291+6dAkfffQR1q1bh4qKirb1cXFx2LNnD/z8/JCUlIRDhw55OroisICJqFNZWVl4/fXXodPpAACtra04evQoLly4AB8fH0RGRmLAgAHIyspCVlYWSktLkZeXhyFDhmDPnj0ICAhAaWkpKisrBf8m8iXrOWCr1YrCwkKMGDECffr0QXR0NEwmEyIiIpCTkyM6Xqe2vZKMQ2WvOLxeDmwtLWiZ9xws6//Ubr2ltAwtz8yBzWwWlKxrSs0t9zHi4+ODkpISFBcXQ6fTwWg0Ytq0adBqtYiOjsajjz4Kg8GAAQMGYOTIkSgsLERjYyN+9rOf4eTJkzAajW3lO2PGDLS0tIj+lWRL1gWcnZ2NFStWIDc3F7t27UJGRgZmzZqFM2fOID4+XnS8XkPy8oJmyYuwfvQJrJVVAABbTQ2sxe9C/asCSP7+YgPeg1Jzy5lGo8H27duRnp6Oq1evYvbs2UhJSUFpaSlu3rzZ4fGnTp3Ciy++CL1ej6+//hr9+/eHVquFyWRi+TpAtlMQW7duxaZNm2A0GmEwGAAAKSkpOHz4MHbs2IG4uDjBCXsX6cGhUP18DiyFr0P6n9fRumoNVFOehCpqjOhoXVJqbrlaunQpJk+ejMuXLyMlJQXHjx93aLuBAwdi5MiRbT+HhYXB19eXBWyHbM+AV65cibS0tLbyvW348OHw8vJCVFQUAOCHH36AwWBAeHg4xowZg6+++kpE3F5BNXUKpNAQtObOB9RqqOZkio7kEKXmlpvIyEgsX74cAJCRkeFw+d5+wS0gIABlZWU4ePAgQkJCsGbNGnfG7RVkWcB1dXU4fvw4pk+f3uG+2tpa6PV6+Pj4AAByc3MxY8YMVFdXY/369Zg5cyaam5vtHkOSJIcWk8nodP5DH7yKt3L6t1v+r3q/0/sxmYwO57x7MRqdzy1JEqSoMcBPP0GVOgmSl5fT+zAaPZtZDrlFjZGejI/OPgmtoKAAXl5e2LBhA/bt2+dQhjvLt7S0FBkZGXj22WfR2tqKrKwsDB48uJPcpm7nVsriKNkWMIAO/3g3btyAyWRqm364fPky9u/fj+zsbADA+PHjERQU5PDgcZeHpyxD3oar7Zag8IlCMznCVlMD6/t/hmrGdFi3vA/bRWV8xqISc8ttjPTv3x8zZ84EAKxevdqhbe4u39tzvidPnkRZWRm8vLza/m9S52RZwIGBgQCA6urqdutfe+01nDt3ru0FuNraWgwaNKjtbBgAHnroIfzjH/+wewybzebQYjAku+4Xc5LBkOxwzruX5GTnctuaW27Nn06bCnV2FqQJ42BZ83vYrFan9pOc7LnMcsktaoz0ZHzcPbU3fvx4+Pr64quvvsKZM2fsHvte5Xvbe++9BwBITU3tJLeh27mVsjhKlgUcFhaGqKgorFy5Eps3b8bevXuRl5eH4uJiAOAVEG5gLX4HkkYDVeZsAIA6fx5s5y/Aur1UcLKuKTW33Nz+P3Xw4EG7j7VXvgDa3ngRFxfn1FPy+40sC1ilUqGkpAR6vR55eXnIyspCYGAg5s+fD7Va3fYCXGhoKC5cuICmpqa2bWtqajB06FBR0RXJWlkF6yefQr3kV5A0ty6Mkfz8oF5cAOvmLbDV1AhO2Dml5pajoKAgAMDp06e7fJwj5QsA58+fh9lshk6ng5+fn1sy9wayvQwtPDy8w1xuZmYmIiMj4evrC+DWVMWECROwceNG5Ofno7y8HGfPnkVKSoqIyACA9JeMTq2XA1VsDFQ7d3RcP1oP1YfyPZNUam45jpHnn38eS5YsaXcy05nAwED4+vo69CaLoUOHoqmpCf/85z9dHbfXkG0Bd6aiogKJiYnt1v3xj3/Es88+i6KiInh7e2Pr1q3w9vYWlJBImVpaWvDTTz/Zfdzu3bvxyCOP4MiRI3av8W1oUN73+3maYgrYbDajuroa+fn57daHhYXhyy+/FJSK6P5z54fuUM8opoD9/f1hsVhExyAichlZvghHRHQ/YAETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSCKuQxNlCEByjx2tFbruiAeOq6ozD09tqgx0pPjxsTEOL3NmdpzAICw0Afa3fbEsXsryebMR/cQ0X1ryeoNAIBVi3Pa3abu4xQEEZEgLGAiIkFYwEREgrCAiYgEYQETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMRCcKPo7RjRwVw9oqYYw8JAKYldG/bRadO4Mi1a64N5IBorRZrIyK7ta2ozEDPcosaIz0ZH0q0cOFCVFVVCTl2TEwMioqKXL5fFrAdZ68Apy+KTuG8I9eu4csrDaJjOEWJmQHljhGlqaqqgslkEh3DpTgFQUQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMR3UGn03nsWLwOmIh6pYSEBKSlpSE+Ph4PPvgg1Go16uvrUVlZifLycuzcuRPNzc3ttklNTcW2bdvwzDPP4OOPP3Z7RhYwEfUqTzzxBF5++WWMHTu20/uTk5Pxwgsv4OLFi1i3bh1WrVqFpqYmpKam4sMPP4Svry8ee+wxjxSwrKcgrFYrCgsLMWLECPTp0wfR0dEwmUyIiIhATk6O6HhEJCP+/v7YtGkTPv74Y4wdOxb19fX4wx/+gKeffhrx8fGIjo5GWloaXnrpJVRVVWHgwIF4+eWXUVlZiXnz5rWV7/r167FgwQKPZJZ1AWdnZ2PFihXIzc3Frl27kJGRgVmzZuHMmTOIj48XHa9T215JxqGyVxxeLwe2lha0zHsOlvV/arfeUlqGlmfmwGY2C0rWNaXmVuIYkTutVovPP/8cc+bMwY0bN1BQUIDg4GD84he/wNatW3H48GEcPXoUn332GV599VXExsYiOTkZJ0+exKhRo7Bu3bq28s3Ly4PNZvNIbtkW8NatW7Fp0ybs3LkTBQUFSElJwbJlyzBu3Di0trYiLi5OdMReQ/LygmbJi7B+9AmslVUAAFtNDazF70L9qwJI/v5iA96DUnOT6/31r39FYmIiampqEBsbi7Vr1+LmzZtdbmMymfDLX/4Sra2tkCQJzc3NWLNmjcfKF5BxAa9cuRJpaWkwGAzt1g8fPhxeXl6IiooCAPz6179GeHg4VCoVtm3bJiJqryA9OBSqn8+BpfB12Boa0LpqDVRTnoQqaozoaF1Sam5yndzcXKSlpeHSpUuYNGkSTp065dB2qamp2L59OzQaDWpqauDt7Y2NGzdCkiQ3J/4XWRZwXV0djh8/junTp3e4r7a2Fnq9Hj4+PgCAtLQ0fPrpp0hKSnLqGJIkObSYTEZX/ErdYjIZHc5592I0Op9bNXUKpNAQtObOB9RqqOZkOr0Po9GzmeWQW9QY6cn46M5y2923PbV09kloffv2xerVqwEA+fn5+OGHHxz62935gtv69esxduxYnD9/HgaDARkZGZ38rU3d+lvZI8urIOrq6gAAgwcPbrf+xo0bMJlMePzxx9vWjR8/3qPZHHHog1fx3SeF7da13DQjdPR/CkrkGEmSIEWNge27w1DNzIDk5SU6kkOUmFupY0RuZs+eDZ1OhwMHDjj8DPju8r095/vb3/4Wb731FvLz8/GXv/zFzclvkeUZcGBgIACgurq63frXXnsN586dc8kLcDabzaHFYEh2et8PT1mGvA1X2y1B4ROd3o/BkOxwzruX5GTnc9tqamB9/89QzZgO65b3Ybvo/IfcJid7NrMccosaIz0ZH91Zbrv7tqeWu6cjAWDWrFkAgHXr1jn0N7tX+QLAli1bYDabkZSUhKCgoLv+1oZu/a3skWUBh4WFISoqCitXrsTmzZuxd+9e5OXlobi4GABkewWEktmaW27Nn06bCnV2FqQJ42BZ83vYrFbR0bqk1NzUc5Iktb0Yv2fPHruP76p8AcBsNuObb74BcOtNHJ4gywJWqVQoKSmBXq9HXl4esrKyEBgYiPnz50OtVre9AEeuYy1+B5JGA1XmbACAOn8ebOcvwLq9VHCyrik1N/VcSEgI+vXrh/Pnz+OinWc99sr3tttfeaTX690RuQNZzgEDQHh4OPbt29duXWZmJiIjI+Hr6ysoVe9krayC9ZNPofnfNyFpbg0Jyc8P6sUFsCx9CaqEOEgPPSQ4ZUdKzU2uYTab8Zvf/AZmO9d79+vXDyUlJQ5d57tr1y5cv34d5eXl7ojcgWwLuDMVFRVITExst2758uV45513cOnSJRw7dgwLFy6EyWTCsGHDhGRMf8no1Ho5UMXGQLVzR8f1o/VQfSjfM0ml5lbiGJGjhoYG/O53v7P7uMbGRsyaNQuTJ0/GggULupyj/eKLL/DFF1+4MmaXZDkF0Rmz2Yzq6uoOb8BYsWIF6urq0NTUhPr6etTV1QkrXyKSp88++wzPP/+8R99k4QjFnAH7+/vDYrGIjkFE5DKKOQMmIuptWMBERIKwgImIBGEBExEJwgImIhKEBUxEJAgLmIhIEMVcByzKkABlHjtaq3VdEA8dV1Tmnh5b1BgROTZFiImJ6dZ2Z2rPAQDCQh9od9sTx7aHBWzHNM98KJLLrY2IFB3BaUrMDCh3jChNUVFRt7ZbsnoDAGDV4px2t+WAUxBERIKwgImIBGEBExEJwgImIhKEBUxEJAgLmIhIEBYwEZEgLGAiIkFYwEREgrCAiYgEYQETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSAs4E78+OOPSE1NxahRo6DX67F06VLRkYgUz2g0Qq/XY/jw4Zg7dy4sFovoSHYtWLAAwcHB0Gjc89HpLOBOaDQarF69Gt9//z0qKyuxf/9+fPDBB6JjESmW1WrF3LlzUVJSgr///e9obGzEli1bRMeya/r06aioqHDb/lnAnXjggQeQkHDraw68vb0RGxuL2tpawamIlOvbb79FUFAQIiNvfetJdnY2tm/fLjiVfRMnTsTgwYPdtn9+JZEdDQ0NKCsrw+7du0VHIfKoVosF7+3YjUbz9Xbr33hne6e3H0sai5HDQjvdV11dHUJCQtp+Dg0NxY8//ujixLd8d7wa+7891mF9Z7kH/nt/zHhyElSS5JYs9vAMuAvNzc1IT0/HggULMHLkSNFxiDxKo1ZjQvxonLtYj3MX69vW33373MV6+Pv5IiIspLPdAABsNptbs94petQwSBLs5r5wuQGPPBwlrHwBFvA9WSwWPP3004iJicGiRYtExyESIjwsBOPiuv6yVN8+Pkh/wgCpiyILCQlpd8ZbW1uL4OBgl+W8k0atxoz/ngSNWt3l41InxCN48AC3ZHAUC/gecnJyoNVqsXbtWtFRiIR6PDkRgf+mu+f9Ux+dCJ22b5f7SEhIQF1dHU6cOAEA2LhxI6ZNm+bSnHcaFBiANMPD97w/NGggkhNj3HZ8R7GAO3HgwAEUFxejoqICsbGxiImJwZtvvgnAs0+liOTA20uDGZNTOn2qHj1qGKJHDbO7D7Vajbfffhvp6ekYNmwY/P39kZmZ6Y64bcYnjMawoUEd1nt5aZAxOQVqlf36y83NRXBwMCwWC4KDgzF//nyXZpRsbBSn7NxTDovFgqmPTuzyKRdRb/P5/grsPXC47ed+/n2xMDsdfn18BKbq2tVGM4qKt+FmU3PbuqmPTkRibNfTKp7CM2AnXG0042DVCdhsNpYv3XcmjYtD8AP/mjOdPtkg6/IFgP79/DHlvya0/RwRFoL/iBklMFF7ii/gY8eO4amnnkJgYCD69OmDESNGYNmyZW45lvGbKthsNqSMi3XL/onkTK1WYcbkFHhp1Bgfr8eIB93zIpqrxUQOx5iIMPj5+uCpx7t+sdDTFD0F8d133yEpKQkhISFYvHgxhg4dipqaGpSXl2Pjxo1dbrtk9QYPpSSi+82qxTkOPU7Rb8RYtGgR+vbti4MHD0Kn+9ertNnZ2QJTERE5RrFnwNevX4dWq8Vzzz2HN954w63HutpoxpoNf0b86HBMS0ty67GI6P6h2DPgK1euwGq1dvti7u5MQRw6chKHjpzs1vGI6P7h6BSEYl+ECwgIgEqlwtmzZ0VHISLqFsVOQQBASkoKTpw4gb/97W/o16+fW45Rtns/Dh35Hi/mzESATuuWYxDR/UmxZ8AAUFhYCLPZjMTERGzatAn79u3Du+++i7lz57pk/1cbzfj26EkkjIlg+RKRyyl2DhgA4uPj8fXXX2P58uV44YUXcPPmTYSEhGDmzJku2X/91UZo+/rxul8icgtFT0F4gtVqhcqB94wTETmLBUxEJAhP7YiIBGEBExEJwgImIhKEBUxEJAgLmIhIEBYwEZEgLGAiIkFYwEREgrCAiYgEYQETEQnCAiYiEoQFTEQkCAuYiEgQFjARkSAsYCIiQVjARESCsICJiARhARMRCcICJiIShAVMRCQIC5iISBAWMBGRICxgIiJBWMBERIKwgImIBGEBExEJwgImIhLk/wE7Af6jntM/CAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 447.652x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not calibrating ancilla\n",
    "# depends on the concrete DJ circuit: x on 0,1\n",
    "dj_left0 = QuantumCircuit(n,n)\n",
    "dj_left1 = QuantumCircuit(n) \n",
    "dj_left2 = QuantumCircuit(n)\n",
    "dj_left3 = QuantumCircuit(n)\n",
    "\n",
    "dj_left0.h(0)\n",
    "\n",
    "dj_left0.h(1)\n",
    "dj_left0.x(0)\n",
    "dj_left0.x(1)\n",
    "dj_left0.x(0)\n",
    "dj_left0.x(1)\n",
    "\n",
    "\n",
    "dj_left0.h(0)\n",
    "dj_left0.h(1)\n",
    "dj_left0.measure([0,1], [0,1])\n",
    "dj_left0.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1238b23c-f19c-4cdc-99a7-4d0bda1662f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/4132221646.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left1 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 447.652x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left1 = QuantumCircuit(n, n)\n",
    "dj_left1.x(0)\n",
    "dj_left1 += dj_left0\n",
    "\n",
    "dj_left1.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0715e227-1285-49c3-8b68-ac2e74f420a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/2252380090.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left2 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left2.x(1)\n",
    "dj_left2 += dj_left0\n",
    "dj_left2.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2df5d154-6a85-43c5-85fd-85acfe12c815",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/810308772.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_left3 += dj_left0\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAACoCAYAAAAcsbLgAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAVDUlEQVR4nO3dfVBU9cIH8O+eZUFoUXwuKfKmAsIVFEFIxTfAt8y6Wiqi3ci8MCBSk6WlRV5nwhjzpbC6U5mSOqa3a7435VOZbJklokCYBdfAi/hQpmiKpgu7+/zBA88sIPvbjeWcQ9/PzJlZz56XLz/P8GXP2bOrsVgsFhAREQmQ5A5ARETqwdIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLnIHULol5WdQev26LPse5umJ9WHhDq27pwi4cKWTAwnw6w3MjHVsXbWOtVy51Xh8AI4fI4sXL0ZJSUmn5xERFRWFvLw8WfatNCwNG0qvX8cXV+rkjmG3C1eAHy/KncI+ah1rNeZW4/FRUlICg8Egd4w/PJ6eIiIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSIKJuLTg4GBMmTMDkyZMxfPhwuLm53XFZrVaLp556CjqdrgsTqgvv0yCibmf8+PFYtGgR7r33Xnh5eVk919DQgFOnTmHTpk3YsWMHbt68CaCpMHbs2IE5c+YgOjoajz76qAzJlY+vNIio2xgwYAAOHz4Mg8GA5ORkeHl54aeffkJBQQE+/fRTfPfdd9BqtRg5ciTeeecdVFZW4qGHHrIqjF9//RVvvPGG3D+KYim6NMxmM9atW4dBgwahR48eGDZsGAwGA8LCwpCeni53vHZZGhrQsPBxmN5+x2q+ae8+NDwyH5b6epmSdeyDVQko3LdKeL4SqHGs1ZgZUMfx8cADD6CsrAwTJkzApUuX8OKLL2LgwIHo168fEhMTMWXKFAwZMgQ9e/bEI488ghMnTqBv377Ys2cPysvLWwpjypQpKCwslPvHUSxFl0ZqaipycnKQkZGBjz/+GHPmzMG8efNQWVmJmJgYueO1S6PTwWX5MzB/+BHMxSUAAEtVFcz5W6F9dik0er28AbsRNY61GjOrwbRp07Bnzx7o9Xr861//wuDBg7Fy5UqcO3euzbI3btzAe++9h5EjR+KJJ55AY2MjgoODYTQaWRgCFFsaO3fuxJYtW3DgwAEsXboUiYmJyM7ORlxcHBobGzF8+HC5I96RZkB/SH+bD9O6V2Gpq0Pj6rWQZvwFUuRQuaN1O2ocazVmVjJfX1+899570Ol0WLt2LZKTk3Hp0iWb60mShHHjxsHFxQUWiwWurq6IjIzsgsTqptjSyM3NxdSpUxEfH281PyQkBDqdruU/99y5c4iPj0doaCiGDh2KL7/8Uo64bUgPzoAmMACNGVmAVgtpforckbotNY61GjMr1ZtvvgkvLy8cPHgQzz77rNA6ra9hrFixAgCwfv16+Pn5OTOu6imyNGpqanD69GkkJSW1ea66uhoREREtb5vLyMhAcnIyKioq8Pbbb2Pu3LkwGo0296HRaISmgoICh34GjUYDTeRQ4NdfIU2cAI0Db+ErKCgQztl6Mhjsz124/yW8me5lNf1PxVG7tmEwOJ5ZrWPtSG65M8t1fACOHyPtfcLtkCFDMH36dFy/fh0ZGRlC+29dGFOmTMFLL72E/fv3o2fPnsjKymons8HhsVbLJEqxpQEAPj4+VvN/++03GAyGllNTly5dwtGjR5GamgoAGD16NHx9fXHkyJGuDdwOS1UVzDv+CSk5CebtO2C5qPzPoR4xIxuZG69aTb6hY+WOZZMax1qNmZV4fCxcuBAAsHXrVtTW1tpcvr3CaL6GsXr1agBAWloa79PogCJLw9vbGwBQUVFhNX/NmjWora1tuQheXV2Nvn37Wt2sM3DgQPznP/+xuQ+LxSI0JSQk2J3fYmxoOk8980FoUxdAMyYOprWvwGI227WdhIQE4Zytp/h4+3N3hvh4xzOrdaztza2EzHIdH4Djx0jrU9UAMHHiRADAtm3bbO63o8IAgG+++QZnz57F3XffjaFDra8vxcfHOzzWaplEKbI0goKCEBkZidzcXGzbtg2HDx9GZmYm8vPzAUCx75xqZs5/FxoXF0gpfwUAaBcthOWnn2HevVfmZN2PGsdajZmVSK/XIzQ0FEajEaWlpR0ua6swmh0/fhyA8n/HyEmRpSFJEnbt2oWIiAhkZmZiwYIF8Pb2RlZWFrRabctF8MDAQPz888+4fft2y7pVVVXo37+/XNFhLi6B+aND0C5/FhqXphvuNR4e0C5bCvO27bBUVcmWrbtR41irMbNS+fj4QJIknD9/vsPrmKKFAQBnz54F0PSOLGqfYj9GJDQ0tM21iZSUFISHh8Pd3R1A02msMWPGYPPmzVi0aBGOHTuGCxcuIDExUY7IAAApOgrSgT1t5w+JgHRQuX9Jzn6hwK75SqDGsVZjZkCZx8ePP/4IvV5v8/qDm5sb+vbtK3Tj3po1a/DKK6+0fLQItaXY0mhPUVERRo0aZTXvrbfewmOPPYa8vDy4urpi586dcHV1lSkhEXUVi8WCGzdu2Fzu5s2bmDZtGkJCQvDtt9/aXJY6pprSqK+vR0VFBRYtWmQ1PygoCF988YVMqYhIDW7evGmzMEiMakpDr9fDZDLJHYOI6A9NkRfCiYhImVgaREQkjKVBRETCWBpERCSMpUFERMJYGkREJEw1b7mVyzBPT1Xu2693Jwbpov2qdazlyq3G4+P37DsqKsqh9Sqrmz79Niiwn9Xjrth3d6Sx2PPxhkREKrP85Y0AgNXL0q0ek2N4eoqIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiISxNIiISBhLg4iIhLE0iIhIGEuDiIiEsTSIiEgYS4OIiITxo9FtWFJ+BqXXr8uy72GenlgfFu7QunuKgAtXOjmQAL/ewMxYx9ZV61jLlVuNxwfw+44RNVq8eDFKSkq6fL9RUVHIy8vr9O2yNGwovX4dX1ypkzuG3S5cAX68KHcK+6h1rNWYW43Hh1qVlJTAYDDIHaPT8PQUEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEZHKubu7w9XVtUv2xfs0iIgUolevXpg1axZGjBiByMhI6PV6GI1GlJeX4+TJk9i3bx8qKyut1nF3d8fBgwdx48YNJCUlwWg0OjUjS4OISGbe3t5YtWoVUlJS4OHh0eb5mJgYPPzww1i7di0OHTqE559/HqWlpS2FMXHiRNTW1sLX1xfnzp1zalZFn54ym81Yt24dBg0ahB49emDYsGEwGAwICwtDenq63PGIiH636dOn47vvvkNGRgY8PDzw2Wef4emnn0ZCQgIiIyMxcuRIpKWlYfv27TAajZg2bRpOnDiBnJwcq8JITEx0emEACi+N1NRU5OTkICMjAx9//DHmzJmDefPmobKyEjExMXLHa5eloQENCx+H6e13rOab9u5DwyPzYamvlylZxz5YlYDCfauE5yuBGsdajZkBdR4fapCamoq9e/eiT58++PzzzzF48GBMnjwZr776KgwGA8rKylBYWIjNmzcjJSUFfn5+eP3116HT6fDCCy9YFUZ5eXmXZFZsaezcuRNbtmzBgQMHsHTpUiQmJiI7OxtxcXFobGzE8OHD5Y7YLo1OB5flz8D84UcwF5cAACxVVTDnb4X22aXQ6PXyBuxG1DjWasxMzjF16lRs3LgRkiQhOzsbkyZNwg8//NDhOnV1dVi2bBmKi4tb5n344YddVhiAgksjNzcXU6dORXx8vNX8kJAQ6HQ6REZGAgD+/ve/IzQ0FJIk4YMPPpAjahuaAf0h/W0+TOtehaWuDo2r10Ka8RdIkUPljtbtqHGs1ZiZOlevXr2wadMmSJKElStXIjc3FxaLxeZ6zdcwoqOjcfnyZZhMJqSmpiIuLq4LUjdRZGnU1NTg9OnTSEpKavNcdXU1IiIi4ObmBqCprQ8dOoTx48fbtQ+NRiM0FRQUOPQzSA/OgCYwAI0ZWYBWC2l+it3bKCgoEM7ZejIYHMv9exkMjmdW61g7klvuzHIdH8DvO0YcmZq1ftxVU3ufcLt8+XL4+fnh2LFjWLVK7PRe64veY8aMwZo1ayBJEjZs2NDOOBscGidbFPnuqZqaGgCAj4+P1fzffvsNBoMB9913X8u80aNHd2k2URqNBprIobCcPAVp7hxodDq5I9lUuP8lnPxondW8hlv1CBwySaZEYtQ41mrMrNbjQ2nc3NyQlpYGAHj66adhNpttrtO6MJqvYbz44otIT0/HPffcg9jYWBQVFTk7vjJfaXh7ewMAKioqrOavWbMGtbW1nXIR3GKxCE0JCQmObb+qCuYd/4SUnATz9h2wXLT/ywsSEhKEc7ae4uPtzz1iRjYyN161mnxDx9q1jfh4xzOrdawdyS13ZrmOD+D3HSOOTM1aP+6qqfUp9kmTJsHb2xvFxcU4fvy4zfG6U2EAwK1bt7BlyxYAwLx581qNc7xD42SLIksjKCgIkZGRyM3NxbZt23D48GFkZmYiPz8fABT7zqlmFmND03nqmQ9Cm7oAmjFxMK19BRaBvyjIPmocazVmps4TG9v0tYWffvqpzWU7Koxmzdtp3q6zKbI0JEnCrl27EBERgczMTCxYsADe3t7IysqCVqttuQiuVOb8d6FxcYGU8lcAgHbRQlh++hnm3XtlTtb9qHGs1ZiZOk9ERAQAoLS0tMPlRAoDQMtXyTZv19kUeU0DAEJDQ3HkyBGreSkpKQgPD4e7u7tMqWwzF5fA/NEhuPzjNWhcmoZX4+EB7bKlMD33AqTY4dAMHChzyu5BjWOtxszUuXbv3o3y8nKcOnWqw+VWrlwpdB9GXV0dcnNzUd9F9/gotjTaU1RUhFGjRlnNW7FiBd5991388ssvKCsrw+LFi2EwGBAcHCxLRik6CtKBPW3nD4mAdFC5f0nOfqHArvlKoMaxVmNmQJ3Hh1K9//77eP/9920ul5OTgwEDBmDlypUd3ofR0NCA7OzszozYIUWenmpPfX09Kioq2tzUl5OTg5qaGty+fRuXL19GTU2NbIVBRNRZbty4gblz53bpjXsiVPNKQ6/Xw2QyyR2DiOgPTTWvNIiISH4sDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImGquU9DLsM8PVW5b7/enRiki/ar1rGWK7cajw+59y2HqKgou9eprK4FAAQF9rN67Oz9imBp2LA+LFzuCA6Z2TUfeNmp1DrWasytxuNDrfLy8uxeZ/nLGwEAq5elWz1WAp6eIiIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLA0iIhLG0iAiImEsDSIiEsbSICIiYSwNIiISxtIgIiJhLI12nD9/HhMnTsTgwYMRERGB5557Tu5IRCSDgoICREREICQkBGlpaTCZTHJHsunJJ5+Ev78/XFyc83VJLI12uLi44OWXX8b333+P4uJiHD16FPv375c7FhF1IbPZjLS0NOzatQtnz57FtWvXsH37drlj2ZSUlISioiKnbZ+l0Y5+/fohNrbpq81cXV0RHR2N6upqmVMRUVc6ceIEfH19ER7e9M2Mqamp2L17t8ypbBs7dix8fHyctn1+3asNdXV12LdvHz755BO5oxCRgJOnK3D0RFmb+Rve3d3mcZ8/eSH5LxMgaTRtlq+pqUFAQEDLvwMDA3H+/HknJAZu3TZi6+7/xq3bRpuZAWDmveMQ4NvHKVls4SuNDhiNRsyePRtPPvkk/vznP8sdh4gEDBscDI0GqL14GbUXL7fMb/3450t1GDcist3CAACLxeL0rM16uLkidmiYzcy1Fy+jX5//kq0wAJbGHZlMJjz88MOIiorCkiVL5I5DRIJctFokPzABLlpth8tNHBMDf5+77/h8QECA1SuL6upq+Pv7d1rO1oYPGYSI0AEdLuPVU4/pk8Y4LYMIlsYdpKenw9PTE+vXr5c7ChHZqa93b0yNH3HH5wN9+yBhVFSH24iNjUVNTQ3OnDkDANi8eTNmzpzZmTGtaDQaPHTvOOjvcm//eQBJ9yegh5ur0zKIYGm046uvvkJ+fj6KiooQHR2NqKgovPbaawC69iUrETludOwQBPf3bTNfp3PBnPsToZU6/vWn1WqxadMmzJ49G8HBwdDr9UhJSXFWXACA3sMds++Lb/e5sfdEIjiw7c/TWkZGBvz9/WEymeDv74+srKxOzaix8LegXQ58dgwmkwkPThkLzR3OhRKRMly9Vo+8/A+sLjA/OGUsRkWHy5jKtj2HvkBh6Q8t/+7r3RuPz38IOifde2EPvtKww9Vr9ThecgYWi4WFQaQCXj31mDH5/68BhAUFYGTUYBkTibl/Qhz+5NUTAKCVJCQ/kKiIwgC6QWmUlZVh1qxZ8Pb2Ro8ePTBo0CBkZ2c7ZV8F35TAYrEgMS7aKdsnos4XFR6CoWFB8HB3w6z74lXxB5+bqw5z7k+ARqPB5HGx8O3rLXekFqo+PXXy5EmMHz8eAQEBWLZsGfr374+qqiocO3YMmzdv7nDd5S9v7KKURETKt3pZutByyni946AlS5bgrrvuwvHjx9GrV6+W+ampqTKmIiLqvlT7SuPmzZvw9PTE448/jg0bNjh1X1ev1WPtxn8iZkgoZk4d79R9EREpmWpfaVy5cgVms9nhm20cOT1VWPqD1TsaiIi6C9HTU6q9EN67d29IkoQLFy7IHYWI6A9DtaenACAxMRFnzpzBv//9b/Ts2dMp+9j3yVEUln6PZ9LnoncvT6fsg4hILVT7SgMA1q1bh/r6eowaNQpbtmzBkSNHsHXrVqSlpXXK9q9eq8eJb39A7NAwFgYREVR8TQMAYmJi8PXXX2PFihV46qmncOvWLQQEBGDu3Lmdsv3LV6/B8y4P3pdBRPR/VH16qiuYzWZINj6jhojoj4KlQUREwvgnNBERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJY2kQEZEwlgYREQljaRARkTCWBhERCWNpEBGRMJYGEREJ+18DOyIGnAE2IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_left3.x(0)\n",
    "dj_left3.x(1)\n",
    "dj_left3 += dj_left0\n",
    "dj_left3.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2a810755-950a-48dc-9738-d89337c8f2ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_calibs1 = [dj_left0, dj_left1, dj_left2, dj_left3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2e8ca807-ea06-466b-bd55-e1b3cf5adb46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for circuit in meas_calibs1:\n",
    "#     print('Circuit',circuit.name)\n",
    "#     print(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c62d3a15-7859-4560-9943-3337fb5f505e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 447.652x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not calibrating ancilla\n",
    "dj_right0 = QuantumCircuit(n,n)\n",
    "dj_right1 = QuantumCircuit(n) \n",
    "dj_right2 = QuantumCircuit(n)\n",
    "dj_right3 = QuantumCircuit(n)\n",
    "\n",
    "dj_right0.x(0)\n",
    "dj_right0.x(1)\n",
    "dj_right0.h(0)\n",
    "dj_right0.h(1)\n",
    "dj_right0.h(0)\n",
    "dj_right0.h(1)\n",
    "dj_right0.x(1)\n",
    "dj_right0.x(0)\n",
    "\n",
    "dj_right0.measure([0,1], [0,1])\n",
    "dj_right0.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "96e0d315-552c-4e0c-920b-24105bb8fefb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/4058473414.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right1 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 447.652x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right1 = QuantumCircuit(n, n)\n",
    "dj_right1.x(0)\n",
    "dj_right1 += dj_right0\n",
    "\n",
    "dj_right1.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2028cb53-df6e-4e98-9bdb-e4d7df726779",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/1107241932.py:2: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right2 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right2.x(1)\n",
    "dj_right2 += dj_right0\n",
    "dj_right2.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "109e1538-491b-4f96-a5f5-731e2dbe4a2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/2247627861.py:3: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  dj_right3 += dj_right0\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 507.852x204.68 with 1 Axes>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dj_right3.x(0)\n",
    "dj_right3.x(1)\n",
    "dj_right3 += dj_right0\n",
    "dj_right3.draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b414280-a7eb-4aa6-83a1-c616cdbb4194",
   "metadata": {},
   "outputs": [],
   "source": [
    "meas_calibs2 = [dj_right0, dj_right1, dj_right2, dj_right3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "835a3fa8-ec36-4cb7-8048-21554f81f946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for circuit in meas_calibs2:\n",
    "#     print('Circuit',circuit.name)\n",
    "#     print(circuit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f2d8a4c-8cef-4b4a-b325-f951c922184a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# aer_sim = Aer.get_backend(\"aer_simulator\")\n",
    "# cal_results = aer_sim.run(meas_calibs1, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ade6759-82e9-428d-ae3a-49a56e623b5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot_histogram(cal_results.get_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fcb4a17e-b678-44de-9578-aaf368aa8f8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1484/1392241283.py:3: DeprecationWarning: Passing a Qobj to Backend.run is deprecated and will be removed in a future release. Please pass in circuits or pulse schedules instead.\n",
      "  results_left = real_backend.run(qobj, shots=10000).result()\n"
     ]
    }
   ],
   "source": [
    "t_qc = transpile(meas_calibs1, real_backend)\n",
    "qobj = assemble(t_qc, shots=10000)\n",
    "results_left = real_backend.run(qobj, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4117ce3f-600b-4579-840c-f258e1735fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_counts_left = results_left.get_counts()\n",
    "plot_histogram(result_counts_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "874d7cbf-3195-4d87-9616-da6e26114921",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9.767e-01 1.000e-02 1.290e-02 4.000e-04]\n",
      "[3.31e-02 9.60e-01 2.00e-04 6.70e-03]\n",
      "[4.570e-02 7.000e-04 9.418e-01 1.180e-02]\n",
      "[0.0015 0.049  0.0323 0.9172]\n",
      "[[9.767e-01 3.310e-02 4.570e-02 1.500e-03]\n",
      " [1.000e-02 9.600e-01 7.000e-04 4.900e-02]\n",
      " [1.290e-02 2.000e-04 9.418e-01 3.230e-02]\n",
      " [4.000e-04 6.700e-03 1.180e-02 9.172e-01]]\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "for qc in meas_calibs1:\n",
    "     counts = results_left.get_counts(qc)\n",
    "     this_array = np.array([counts.get('00') or 0, counts.get('01') or 0, counts.get('10') or 0, counts.get('11') or 0])\n",
    "     this_array = this_array/10000\n",
    "     ls.append(this_array)\n",
    "     print(this_array)\n",
    "array = np.array(ls)\n",
    "\n",
    "array_t_left = np.transpose(array)\n",
    "print(array_t_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7f7dd4ad-56f2-4abc-afaf-35baa21a283c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9767 & 0.0331 & 0.0457 & 0.0015  \\\\\n",
       " 0.01 & 0.96 & 0.0007 & 0.049  \\\\\n",
       " 0.0129 & 0.0002 & 0.9418 & 0.0323  \\\\\n",
       " 0.0004 & 0.0067 & 0.0118 & 0.9172  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(array_t_left)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b2a58f85-b4a6-405c-aeda-cddfe5ba7b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_qc = transpile(meas_calibs2, real_backend)\n",
    "qobj = assemble(t_qc, shots=10000)\n",
    "results_right = real_backend.run(qobj, shots=10000).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "239db5d9-ba2c-42cd-a6a0-765e5d4fcff1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'00': 9752, '01': 100, '10': 145, '11': 3}, {'00': 311, '01': 9614, '10': 4, '11': 71}, {'00': 484, '01': 7, '10': 9415, '11': 94}, {'00': 13, '01': 466, '10': 271, '11': 9250}]\n"
     ]
    }
   ],
   "source": [
    "result_counts_right = results_right.get_counts()\n",
    "print(result_counts_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f85b1e6a-6f91-427d-93fa-cec41cd65200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[9.752e-01 3.110e-02 4.840e-02 1.300e-03]\n",
      " [1.000e-02 9.614e-01 7.000e-04 4.660e-02]\n",
      " [1.450e-02 4.000e-04 9.415e-01 2.710e-02]\n",
      " [3.000e-04 7.100e-03 9.400e-03 9.250e-01]]\n"
     ]
    }
   ],
   "source": [
    "ls = []\n",
    "for qc in meas_calibs2:\n",
    "     counts = results_right.get_counts(qc)\n",
    "     this_array = np.array([counts.get('00') or 0, counts.get('01') or 0, counts.get('10') or 0, counts.get('11') or 0])\n",
    "     this_array = this_array/10000\n",
    "     ls.append(this_array)\n",
    "     # print(this_array)\n",
    "array = np.array(ls)\n",
    "\n",
    "array_t_right = np.transpose(array)\n",
    "print(array_t_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "42ba3a02-4ceb-4b0b-bf46-7b50ab52862f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.9752 & 0.0311 & 0.0484 & 0.0013  \\\\\n",
       " 0.01 & 0.9614 & 0.0007 & 0.0466  \\\\\n",
       " 0.0145 & 0.0004 & 0.9415 & 0.0271  \\\\\n",
       " 0.0003 & 0.0071 & 0.0094 & 0.925  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(array_t_right)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ebabfb5d-e4d7-4d10-b5aa-74f791e2f70b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.97595 & 0.0321 & 0.04705 & 0.0014  \\\\\n",
       " 0.01 & 0.9607 & 0.0007 & 0.0478  \\\\\n",
       " 0.0137 & 0.0003 & 0.94165 & 0.0297  \\\\\n",
       " 0.00035 & 0.0069 & 0.0106 & 0.9211  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_sum = (array_t_left + array_t_right) / 2\n",
    "array_to_latex(array_sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "94cff422-48c3-4507-9264-8200c4cf6996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "1.02571 & -0.03427 & -0.05125 & 0.00187  \\\\\n",
       " -0.01066 & 1.04165 & 0.00037 & -0.05405  \\\\\n",
       " -0.01492 & 0.00041 & 1.0631 & -0.03428  \\\\\n",
       " -0.00014 & -0.00779 & -0.01222 & 1.08646  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.linalg as la\n",
    "array_inv = la.inv(array_sum)\n",
    "array_to_latex(array_inv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8e73c15a-e139-4cd5-9d2e-919c8ad9bb59",
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = realqc_results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0b31c25a-4621-439e-9a8c-0a0b5c2490ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "result_array = np.array([counts.get('00') or 0, counts.get('01') or 0, counts.get('10') or 0, counts.get('11') or 0])\n",
    "result_array = result_array/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "53468953-918d-40d8-9845-cba1a25b335f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.023, 0.041, 0.052, 0.884])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2c0d8d33-34fd-4597-b998-9b2274343782",
   "metadata": {},
   "outputs": [],
   "source": [
    "mitigated = np.dot(array_inv, result_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0cf510c1-846c-4c1a-a245-bde6194a9a4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.02118 & -0.0053 & 0.02465 & 0.95947  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(mitigated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e1ab5df7-b59b-4e4f-a7bf-6d45a2274409",
   "metadata": {},
   "outputs": [],
   "source": [
    "def NormalizeData(data):\n",
    "    return (data - np.min(data)) / (np.max(data) - np.min(data))\n",
    "normal = NormalizeData(mitigated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "686352a5-a735-4c3b-bb08-8ad1f2369acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.02744299, 0.        , 0.03104754, 1.        ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "683f74a4-b21d-4db9-ae01-54b3e03cfc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Normalize(arr):\n",
    "    return arr/sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "6b91840d-56ad-4fe2-9375-5accfa19c11c",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_normal = Normalize(mitigated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68ff0b5d-95ff-4394-bca7-0f72c73648b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$\n",
       "\n",
       "\\begin{bmatrix}\n",
       "0.02118 & -0.0053 & 0.02465 & 0.95947  \\\\\n",
       " \\end{bmatrix}\n",
       "$$"
      ],
      "text/plain": [
       "<IPython.core.display.Latex object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_to_latex(new_normal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ad98061-aa3e-4f54-9063-323c23377b6f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}