hackthon.002 copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modules import completed...\n",
      "------------------------------\n",
      " ### Init IBMQ and Set up provider\n",
      "IBMQ loading completed...\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from math import pi\n",
    "# qiskit\n",
    "from qiskit import (\n",
    "    IBMQ, execute, transpile,\n",
    "    QuantumRegister, ClassicalRegister, QuantumCircuit,\n",
    ")\n",
    "# qurry\n",
    "sys.path.insert(0, './')\n",
    "from backend import backendWrapper\n",
    "from mori import TagMap\n",
    "print(\"Modules import completed...\")\n",
    "print(\"-\"*30+\"\\n ### Init IBMQ and Set up provider\")\n",
    "IBMQ.load_account()\n",
    "IBMQ.providers()\n",
    "provider = IBMQ.get_provider(\n",
    "    hub='ibm-q-hub-ntu', group='ntu-internal', project='default')\n",
    "backend = backendWrapper(provider)\n",
    "print(\"IBMQ loading completed...\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32768\n"
     ]
    }
   ],
   "source": [
    "qaoaQurry = Qurry()\n",
    "qaoaName = 'tsp'\n",
    "problesSize = 2\n",
    "\n",
    "nqubits = 3 ** 2\n",
    "sampling = 10\n",
    "shot = 2**(nqubits+6)\n",
    "print(shot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable\n",
    "from collections import defaultdict\n",
    "\n",
    "def H_Ising(bitstring, Ising_coeff):\n",
    "    spins = []\n",
    "    for bit in bitstring:\n",
    "        if bit == '0':\n",
    "            spins.append(-1)\n",
    "        elif bit == '1':\n",
    "            spins.append(1)\n",
    "    H = 0\n",
    "    for i in range(0, nqubits):\n",
    "        H += Ising_coeff[i][i]*spins[i]\n",
    "        for j in range(i+1, nqubits):\n",
    "            H += Ising_coeff[i][j]*spins[i]*spins[j]\n",
    "    return H\n",
    "\n",
    "def compute_expectation(counts: dict[str, int], Ising_coeff: np.ndarray):   \n",
    "    \"\"\"\n",
    "    Computes expectation value based on measurement results\n",
    "    \"\"\"   \n",
    "    sum = 0\n",
    "    sum_count = 0\n",
    "    for bitstring, count in counts.items():\n",
    "        sum += count*H_Ising(bitstring, Ising_coeff)\n",
    "        sum_count += count\n",
    "    return sum/sum_count\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "\n",
    "# We will also bring the different circuit components that\n",
    "# build the qaoa circuit under a single function\n",
    "def create_qaoa_circ(\n",
    "    Ising_coeff: np.ndarray, \n",
    "    theta: list[float],\n",
    ") -> QuantumCircuit: \n",
    "    \"\"\"\n",
    "    Creates a parametrized qaoa circuit\n",
    "    \"\"\"   \n",
    "    p = len(theta)//2  # number of alternating unitaries\n",
    "    qc = QuantumCircuit(nqubits)\n",
    "    \n",
    "    beta = theta[:p]\n",
    "    gamma = theta[p:]\n",
    "    \n",
    "    # initial_state\n",
    "    for i in range(0, nqubits):\n",
    "        qc.h(i)\n",
    "    \n",
    "    for irep in range(0, p):\n",
    "        \n",
    "        # problem unitary\n",
    "        for i in range(nqubits):\n",
    "            for j in range(nqubits):\n",
    "                if Ising_coeff != 0 and i!=j:\n",
    "                    qc.rzz(2 * Ising_coeff[i][j]*gamma[irep], i, j)\n",
    "                    \n",
    "        for i in range(nqubits):\n",
    "            qc.rz(2 * Ising_coeff[i][i]*gamma[irep], i)\n",
    "        \n",
    "        # mixer unitary\n",
    "        for i in range(nqubits):\n",
    "            qc.rx(2 * beta[irep], i)\n",
    "            \n",
    "    qc.measure_all()\n",
    "        \n",
    "    return qc\n",
    "\n",
    "def qaoa_circs(\n",
    "    Ising_coeff: np.ndarray,\n",
    ") -> Callable[[list[float]], QuantumCircuit]:\n",
    "    \n",
    "    def theta_depend(theta: list[float]) -> QuantumCircuit:\n",
    "        return create_qaoa_circ(\n",
    "            Ising_coeff=Ising_coeff, \n",
    "            theta=theta,\n",
    "        )\n",
    "    \n",
    "    return theta_depend\n",
    "\n",
    "def expect(\n",
    "    Ising_coeff: np.ndarray,\n",
    ") -> Callable[[dict[str, int]], float]:\n",
    "    \n",
    "    def counts_depend(counts: dict[str, int]) -> float:\n",
    "        return compute_expectation(\n",
    "            counts=counts, \n",
    "            Ising_coeff=Ising_coeff,\n",
    "        )\n",
    "    \n",
    "    return counts_depend\n",
    "\n",
    "def countCombine(countList: list[dict[str, int]]) -> dict[str, int]:\n",
    "    summary = defaultdict(int)\n",
    "    for c in countList:\n",
    "        for k, v in c.items():\n",
    "            summary[k] += v\n",
    "    return summary\n",
    "\n",
    "def expecting(Ising_matrix: np.ndarray) -> Callable[[list[float]], float]:\n",
    "    \n",
    "    qaoa_circs_giver = qaoa_circs(Ising_matrix)\n",
    "    expectation_giver = expect(Ising_matrix)\n",
    "    # Finally we write a function that executes the circuit on the chosen backend\n",
    "    def get_expectation(theta: list[float]) -> float:\n",
    "        \"\"\"\n",
    "        Runs parametrized circuit and get expectation value\n",
    "    \n",
    "        \"\"\"\n",
    "        # waveName = qaoaQurry.addWave(qaoa_circs_giver(theta))\n",
    "        # singleIterResult = qaoaQurry.multiOutput(\n",
    "        #     configList=[{ \n",
    "        #         'wave': waveName,\n",
    "        #         'tags': waveName,\n",
    "        #         'shots': 2**(nqubits+3),\n",
    "        #         'sampling': sampling,\n",
    "        #     }],\n",
    "        #     backend=backend('aer_gpu'),\n",
    "        #     saveLocation=Path('test.004')\n",
    "        # )\n",
    "        # countsList: list[dict[str, int]] = singleIterResult['tagMapCounts'][waveName]\n",
    "        # counts = countCombine(countsList)\n",
    "        \n",
    "        qc = qaoa_circs_giver(theta)\n",
    "        counts = backend('aer_gpu').run(qc, shots=2**16).result().get_counts()\n",
    "        clear_output()\n",
    "        print(counts)\n",
    "        global ccc\n",
    "        ccc = counts\n",
    "\n",
    "        return expectation_giver(counts)\n",
    "    \n",
    "    return get_expectation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "ccc = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.1062233448028564,\n",
       " 3.1053903102874756,\n",
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       " 3.1150877475738525,\n",
       " 3.1708669662475586,\n",
       " 3.181145668029785]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "torch.manual_seed(0)\n",
    "theta = torch.empty(int(nqubits)*2)\n",
    "torch.nn.init.normal_(theta, mean=np.pi, std=0.01*np.pi)\n",
    "theta = theta.tolist()\n",
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'100100110': 1, '010010110': 2, '101000011': 3, '000011110': 2, '110000101': 1, '111010010': 4, '100111111': 4, '110111111': 1, '111010001': 2, '010110111': 6, '000101011': 2, '001011110': 8, '111001111': 5, '111000111': 5, '010111010': 10, '011011111': 9, '111111011': 2, '001111011': 5, '111001110': 7, '100110101': 12, '011000101': 1, '101101111': 3, '011001111': 6, '110100110': 14, '110011101': 13, '110010011': 11, '111001010': 8, '101001110': 2, '000100111': 4, '011011011': 7, '010111011': 7, '101100111': 4, '001001011': 4, '110100111': 5, '110110011': 4, '011010101': 4, '011100011': 17, '110101111': 7, '011011010': 8, '100101110': 16, '111001011': 10, '111100011': 10, '000101110': 4, '110110111': 5, '111010111': 10, '101101101': 9, '111100100': 6, '010111111': 4, '111011111': 2, '111101110': 11, '110010111': 3, '000010111': 3, '011010010': 27, '011111110': 8, '100100111': 10, '011100111': 13, '001001111': 6, '000101111': 5, '101011011': 20, '010011111': 8, '011110101': 18, 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'101111010': 24, '010110100': 36, '101100010': 30, '000111000': 87, '110000001': 183, '110011100': 53, '010100111': 34, '010110001': 38, '011111010': 40, '100001011': 67, '101011101': 27, '111011101': 10, '100110111': 8, '111001100': 2, '011101100': 45, '000111111': 4, '011100101': 27, '010111100': 5, '011101001': 38, '100111101': 9, '111011110': 14, '010110011': 17, '011101011': 29, '101101001': 5, '011111001': 39, '011001101': 12, '011100110': 19, '110000000': 674, '110110100': 7}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "     fun: -31178.832313510597\n",
       "   maxcv: 0.0\n",
       " message: 'Optimization terminated successfully.'\n",
       "    nfev: 170\n",
       "  status: 1\n",
       " success: True\n",
       "       x: array([8.10925447, 3.22208742, 3.1439113 , 3.13348564, 3.14359837,\n",
       "       3.11219026, 3.11529214, 3.2116978 , 3.12887284, 3.15200216,\n",
       "       3.12514276, 3.1246901 , 3.15226629, 3.2171216 , 3.18979418,\n",
       "       3.12408961, 3.18073589, 8.11393103])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.optimize import minimize\n",
    "\n",
    "expectation = expecting(Ising_matrix=aaaa)\n",
    "\n",
    "res = minimize(expectation, \n",
    "                theta, options ={'iprint':1, 'disp':False, 'maxiter':10000,\n",
    "                                 'catol':0.002, 'rhobeg':5.0},\n",
    "            method='COBYLA')\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filterCounts(counts: dict[str, int]) -> dict[str, int]:\n",
    "    bitLen = len(list(counts)[0])\n",
    "    qubitLen = int(np.sqrt(bitLen))\n",
    "    if (qubitLen % 1 > 0):\n",
    "        raise ValueError(f\"bitLen: {bitLen}, qubitLen: {qubitLen}.\")\n",
    "\n",
    "    def filter(bitstr: str) -> bool:\n",
    "        strList = [bitstr[qubitLen*(k):qubitLen*(k+1)] for k in range(qubitLen)]\n",
    "        strList1Sum = [sum([int(b) for b in bs]) for bs in strList]\n",
    "        strList1Pos = [[i for i, b in enumerate(bs) if b == '1'] for bs in strList]\n",
    "        # print(strList1Pos)\n",
    "        strList1PosOnly = all([len(lbs) == 1 for lbs in strList1Pos])\n",
    "        # print(strList1Pos, strList1PosOnly)\n",
    "        strList1PosImmutable = sum([sum(l) for l in strList1Pos]) == (qubitLen-1)*qubitLen/2\n",
    "        # print(strList1PosImmutable)\n",
    "            \n",
    "        return all([ s==1 for s in strList1Sum]) and strList1PosImmutable\n",
    "    \n",
    "    filteredCounts = {\n",
    "        k: v\n",
    "    for k, v in counts.items() if filter(k) }\n",
    "    \n",
    "    return filteredCounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.expecting.<locals>.get_expectation(theta: list) -> float>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expectation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(65536, 2438, 0.037200927734375)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(ccc.values()), sum(filterCounts(ccc).values()), sum(filterCounts(ccc).values())/sum(ccc.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def maxCounts(_counts: dict[str, int]) -> str:\n",
    "    vs = list(_counts.values())\n",
    "    vsMax = max(_counts.values())\n",
    "    vsMaxIndex = list(_counts.keys())[vs.index(vsMax)]\n",
    "    return vsMaxIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'000000000'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxCounts(ccc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5038"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccc[maxCounts(ccc)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'100100110': 1,\n",
       " '010010110': 2,\n",
       " '101000011': 3,\n",
       " '000011110': 2,\n",
       " '110000101': 1,\n",
       " '111010010': 4,\n",
       " '100111111': 4,\n",
       " '110111111': 1,\n",
       " '111010001': 2,\n",
       " '010110111': 6,\n",
       " '000101011': 2,\n",
       " '001011110': 8,\n",
       " '111001111': 5,\n",
       " '111000111': 5,\n",
       " '010111010': 10,\n",
       " '011011111': 9,\n",
       " '111111011': 2,\n",
       " '001111011': 5,\n",
       " '111001110': 7,\n",
       " '100110101': 12,\n",
       " '011000101': 1,\n",
       " '101101111': 3,\n",
       " '011001111': 6,\n",
       " '110100110': 14,\n",
       " '110011101': 13,\n",
       " '110010011': 11,\n",
       " '111001010': 8,\n",
       " '101001110': 2,\n",
       " '000100111': 4,\n",
       " '011011011': 7,\n",
       " '010111011': 7,\n",
       " '101100111': 4,\n",
       " '001001011': 4,\n",
       " '110100111': 5,\n",
       " '110110011': 4,\n",
       " '011010101': 4,\n",
       " '011100011': 17,\n",
       " '110101111': 7,\n",
       " '011011010': 8,\n",
       " '100101110': 16,\n",
       " '111001011': 10,\n",
       " '111100011': 10,\n",
       " '000101110': 4,\n",
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       " '011111101': 14,\n",
       " '101000110': 3,\n",
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       " '100110001': 29,\n",
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       " '111011011': 16,\n",
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       " '010010011': 5,\n",
       " '101110011': 17,\n",
       " '010011010': 45,\n",
       " '001001101': 4,\n",
       " '110010100': 33,\n",
       " '110000111': 5,\n",
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       " '111111110': 7,\n",
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       " '011010011': 23,\n",
       " '001111110': 5,\n",
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       " '010011011': 15,\n",
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       " '001010010': 258,\n",
       " '001110100': 151,\n",
       " '000110101': 3,\n",
       " '001100111': 44,\n",
       " '111111001': 64,\n",
       " '110010010': 49,\n",
       " '110101000': 295,\n",
       " '101100101': 16,\n",
       " '001110010': 147,\n",
       " '010101001': 149,\n",
       " '100000010': 531,\n",
       " '101100110': 11,\n",
       " '010011101': 7,\n",
       " '010010001': 196,\n",
       " '000000000': 5038,\n",
       " '110100101': 10,\n",
       " '001101100': 37,\n",
       " '101001001': 47,\n",
       " '000001011': 136,\n",
       " '110110010': 5,\n",
       " '000001110': 141,\n",
       " '111110000': 64,\n",
       " '100111011': 8,\n",
       " '111001001': 6,\n",
       " '011100001': 142,\n",
       " '111011001': 42,\n",
       " '010011110': 11,\n",
       " '001110011': 38,\n",
       " '010100001': 379,\n",
       " '000011100': 172,\n",
       " '110010000': 154,\n",
       " '110011111': 10,\n",
       " '100100100': 90,\n",
       " '001110101': 29,\n",
       " '100010010': 256,\n",
       " '011110110': 18,\n",
       " '111110110': 18,\n",
       " '000100101': 148,\n",
       " '001001010': 172,\n",
       " '110100000': 163,\n",
       " '110000100': 134,\n",
       " '000010110': 160,\n",
       " '100001101': 39,\n",
       " '001111010': 5,\n",
       " '111100111': 8,\n",
       " '101111110': 11,\n",
       " '011110000': 284,\n",
       " '011011000': 147,\n",
       " '010001111': 32,\n",
       " '111111100': 65,\n",
       " '010010010': 67,\n",
       " '000100100': 440,\n",
       " '000110000': 698,\n",
       " '110100100': 50,\n",
       " '010000010': 410,\n",
       " '110110001': 10,\n",
       " '000111011': 4,\n",
       " '011111100': 22,\n",
       " '010011100': 33,\n",
       " '101001011': 7,\n",
       " '010001100': 383,\n",
       " '010111001': 6,\n",
       " '001101110': 5,\n",
       " '011000000': 659,\n",
       " '001010100': 403,\n",
       " '011100000': 265,\n",
       " '110001000': 234,\n",
       " '100000011': 133,\n",
       " '111101111': 1,\n",
       " '111101001': 38,\n",
       " '110010001': 36,\n",
       " '011010000': 158,\n",
       " '000000001': 1157,\n",
       " '110001100': 142,\n",
       " '100010101': 69,\n",
       " '001110111': 10,\n",
       " '111011000': 58,\n",
       " '101011010': 47,\n",
       " '000001111': 8,\n",
       " '101110101': 33,\n",
       " '011100100': 123,\n",
       " '100011010': 130,\n",
       " '100011101': 33,\n",
       " '001110001': 165,\n",
       " '111001000': 94,\n",
       " '100000100': 454,\n",
       " '110011110': 23,\n",
       " '011011001': 5,\n",
       " '001000011': 121,\n",
       " '000001100': 541,\n",
       " '001101000': 159,\n",
       " '010100110': 33,\n",
       " '010101111': 25,\n",
       " '101001111': 7,\n",
       " '100001100': 259,\n",
       " '110100001': 39,\n",
       " '101110010': 53,\n",
       " '100011111': 24,\n",
       " '001000010': 516,\n",
       " '100010000': 850,\n",
       " '100010111': 41,\n",
       " '010000100': 579,\n",
       " '011000110': 2,\n",
       " '010001000': 825,\n",
       " '000110111': 13,\n",
       " '010111000': 82,\n",
       " '000111100': 1,\n",
       " '100000000': 1162,\n",
       " '001101101': 14,\n",
       " '010100011': 45,\n",
       " '101000000': 692,\n",
       " '001011011': 17,\n",
       " '100011100': 145,\n",
       " '100100000': 730,\n",
       " '100101000': 156,\n",
       " '000000100': 1191,\n",
       " '111111101': 2,\n",
       " '010110110': 16,\n",
       " '011010110': 12,\n",
       " '100101010': 28,\n",
       " '001010000': 870,\n",
       " '010010100': 211,\n",
       " '110001111': 19,\n",
       " '101000001': 121,\n",
       " '010000110': 139,\n",
       " '010110000': 185,\n",
       " '111101100': 31,\n",
       " '100001010': 429,\n",
       " '101011100': 57,\n",
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       " '011001011': 15,\n",
       " '000100000': 1162,\n",
       " '101010111': 22,\n",
       " '001100001': 281,\n",
       " '010100000': 863,\n",
       " '011000111': 8,\n",
       " '101011110': 25,\n",
       " '111100001': 7,\n",
       " '010001010': 258,\n",
       " '110111011': 8,\n",
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       " '110111100': 37,\n",
       " '100111110': 14,\n",
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       " '001110110': 24,\n",
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       " '000001001': 434,\n",
       " '101011000': 321,\n",
       " '101101100': 8,\n",
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       " '000000010': 1175,\n",
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       " '000101100': 128,\n",
       " '001101010': 24,\n",
       " '010000101': 136,\n",
       " '011101010': 61,\n",
       " '011001100': 28,\n",
       " '100110011': 7,\n",
       " '110001110': 24,\n",
       " '101010010': 147,\n",
       " '100001111': 49,\n",
       " '001011000': 163,\n",
       " '010101110': 30,\n",
       " '100011110': 36,\n",
       " '001010011': 38,\n",
       " '110111001': 23,\n",
       " '110101011': 19,\n",
       " '001111001': 4,\n",
       " '000011001': 135,\n",
       " '010100100': 278,\n",
       " '001110000': 247,\n",
       " '000101000': 629,\n",
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       " '111110101': 13,\n",
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       " '011110111': 9,\n",
       " '011001010': 35,\n",
       " '000000110': 379,\n",
       " '000011010': 120,\n",
       " '000010101': 162,\n",
       " '011101111': 10,\n",
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       " '010011000': 172,\n",
       " '100000111': 1,\n",
       " '101001100': 21,\n",
       " '101111000': 54,\n",
       " '100010001': 379,\n",
       " '110111000': 62,\n",
       " '000100010': 561,\n",
       " '001100011': 67,\n",
       " '010000001': 532,\n",
       " '110011001': 56,\n",
       " '010000000': 1179,\n",
       " '101100000': 184,\n",
       " '010101010': 154,\n",
       " '110001011': 24,\n",
       " '110111010': 35,\n",
       " '011100010': 147,\n",
       " '110100010': 29,\n",
       " '110101010': 54,\n",
       " '000000011': 383,\n",
       " '101100100': 62,\n",
       " '110110101': 3,\n",
       " '101110001': 44,\n",
       " '010110010': 48,\n",
       " '110110110': 4,\n",
       " '001001100': 190,\n",
       " '101000100': 135,\n",
       " '010100101': 55,\n",
       " '001101011': 19,\n",
       " '100101101': 12,\n",
       " '101101011': 5,\n",
       " '100100001': 192,\n",
       " '000100110': 150,\n",
       " '111110100': 23,\n",
       " '101010000': 225,\n",
       " '111110001': 26,\n",
       " '011000100': 176,\n",
       " '001100100': 250,\n",
       " '100110100': 41,\n",
       " '111101011': 8,\n",
       " '011101101': 26,\n",
       " '100101001': 31,\n",
       " '100011001': 134,\n",
       " '101111011': 12,\n",
       " '001001000': 727,\n",
       " '111010100': 4,\n",
       " '100101111': 7,\n",
       " '101011001': 42,\n",
       " '111111010': 79,\n",
       " '101111111': 2,\n",
       " '100111001': 4,\n",
       " '101100011': 7,\n",
       " '001010111': 41,\n",
       " '111110011': 19,\n",
       " '011000001': 116,\n",
       " '010101100': 137,\n",
       " '001011111': 4,\n",
       " '010001110': 55,\n",
       " '110101100': 38,\n",
       " '101000010': 172,\n",
       " '010000011': 122,\n",
       " '000110010': 130,\n",
       " '011101110': 15,\n",
       " '111000000': 86,\n",
       " '110001101': 24,\n",
       " '000100001': 532,\n",
       " '010111101': 14,\n",
       " '101111100': 44,\n",
       " '000010010': 389,\n",
       " '101010011': 46,\n",
       " '011110010': 53,\n",
       " '101010110': 28,\n",
       " '100001110': 56,\n",
       " '100001001': 250,\n",
       " '001111111': 8,\n",
       " '001001001': 73,\n",
       " '100010011': 64,\n",
       " '000001101': 144,\n",
       " '100011000': 201,\n",
       " '110100011': 13,\n",
       " '111100000': 88,\n",
       " '111010000': 79,\n",
       " '000101001': 113,\n",
       " '011010001': 27,\n",
       " '010001011': 36,\n",
       " '011011110': 11,\n",
       " '111010101': 8,\n",
       " '100010100': 277,\n",
       " '001000001': 417,\n",
       " '000010000': 1219,\n",
       " '101110100': 46,\n",
       " '110101001': 49,\n",
       " '111011100': 21,\n",
       " '001100000': 847,\n",
       " '111101000': 43,\n",
       " '111000110': 1,\n",
       " '100110110': 17,\n",
       " '010110101': 4,\n",
       " '100010110': 41,\n",
       " '101001101': 15,\n",
       " '111011010': 38,\n",
       " '100000110': 133,\n",
       " '000010001': 538,\n",
       " '110001010': 151,\n",
       " '000010011': 149,\n",
       " '110010110': 17,\n",
       " '011110001': 44,\n",
       " '001011010': 26,\n",
       " '110110000': 122,\n",
       " '010101000': 217,\n",
       " '100100010': 195,\n",
       " '010101011': 25,\n",
       " '101101000': 147,\n",
       " '001010110': 69,\n",
       " '011000010': 117,\n",
       " '100101011': 1,\n",
       " '001111000': 88,\n",
       " '001101111': 4,\n",
       " '011010111': 7,\n",
       " '100000101': 140,\n",
       " '001010001': 278,\n",
       " '001000100': 527,\n",
       " '100001000': 881,\n",
       " '001011100': 40,\n",
       " '101011111': 16,\n",
       " '100011011': 27,\n",
       " '101110111': 9,\n",
       " '110101101': 25,\n",
       " '101010101': 25,\n",
       " '100110000': 180,\n",
       " '001101001': 48,\n",
       " '110101110': 23,\n",
       " '100111100': 15,\n",
       " '000110011': 2,\n",
       " '011110011': 20,\n",
       " '010101101': 24,\n",
       " '001010101': 59,\n",
       " '011001000': 179,\n",
       " '011001110': 9,\n",
       " '000100011': 158,\n",
       " '101000111': 7,\n",
       " '101001010': 40,\n",
       " '011111000': 85,\n",
       " '011001001': 42,\n",
       " '011010100': 25,\n",
       " '111111111': 12,\n",
       " '001100110': 64,\n",
       " '110011010': 43,\n",
       " '101111010': 24,\n",
       " '010110100': 36,\n",
       " '101100010': 30,\n",
       " '000111000': 87,\n",
       " '110000001': 183,\n",
       " '110011100': 53,\n",
       " '010100111': 34,\n",
       " '010110001': 38,\n",
       " '011111010': 40,\n",
       " '100001011': 67,\n",
       " '101011101': 27,\n",
       " '111011101': 10,\n",
       " '100110111': 8,\n",
       " '111001100': 2,\n",
       " '011101100': 45,\n",
       " '000111111': 4,\n",
       " '011100101': 27,\n",
       " '010111100': 5,\n",
       " '011101001': 38,\n",
       " '100111101': 9,\n",
       " '111011110': 14,\n",
       " '010110011': 17,\n",
       " '011101011': 29,\n",
       " '101101001': 5,\n",
       " '011111001': 39,\n",
       " '011001101': 12,\n",
       " '011100110': 19,\n",
       " '110000000': 674,\n",
       " '110110100': 7}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'010100001': 379,\n",
       " '010001100': 383,\n",
       " '001010100': 403,\n",
       " '100001010': 429,\n",
       " '001100010': 398,\n",
       " '100010001': 379}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def filterCounts(counts: dict[str, int]) -> dict[str, int]:\n",
    "    bitLen = len(list(counts)[0])\n",
    "    qubitLen = int(np.sqrt(bitLen))\n",
    "    if (qubitLen % 1 > 0):\n",
    "        raise ValueError(f\"bitLen: {bitLen}, qubitLen: {qubitLen}.\")\n",
    "\n",
    "    def filter(bitstr: str) -> bool:\n",
    "        strList = [bitstr[qubitLen*(k):qubitLen*(k+1)] for k in range(qubitLen)]\n",
    "        strList1Sum = [sum([int(b) for b in bs]) for bs in strList]\n",
    "        strList1PosImmutable = dict.fromkeys(strList)\n",
    "            \n",
    "        return all([ s==1 for s in strList1Sum]) and len(strList1PosImmutable) == 3\n",
    "    \n",
    "    filteredCounts = {\n",
    "        k: v\n",
    "    for k, v in counts.items() if filter(k) }\n",
    "    \n",
    "    return filteredCounts\n",
    "\n",
    "filterCounts(ccccc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filterCounts({'010010010': 33})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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