hackthon.003.IsingDataGenerator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prpepred function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nqubits = len(Ising_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import clear_output\n",
    "from typing import Callable\n",
    "from collections import defaultdict\n",
    "from scipy.optimize import minimize\n",
    "import torch\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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\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",
    "\n",
    "def expecting(Ising_matrix: np.ndarray) -> Callable[[list[float]], float]:\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 lastCounts\n",
    "        lastCounts = counts\n",
    "\n",
    "        return expectation_giver(counts)\n",
    "    \n",
    "    return get_expectation\n",
    "\n",
    "\n",
    "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\n",
    "\n",
    "\n",
    "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",
    "        # print(strList, strList1Sum, strList1PosImmutable, qubitLen)\n",
    "        \n",
    "        return all([ s==1 for s in strList1Sum]) and len(strList1PosImmutable) == qubitLen\n",
    "    \n",
    "    filteredCounts = {\n",
    "        k: v\n",
    "    for k, v in counts.items() if filter(k) }\n",
    "    \n",
    "    return filteredCounts\n",
    "\n",
    "\n",
    "def thetaGen(length: int) -> list[float]:\n",
    "    torch.manual_seed(0)\n",
    "    theta = torch.empty(length*2)\n",
    "    torch.nn.init.normal_(theta, mean=np.pi, std=0.01*np.pi)\n",
    "    theta = theta.tolist()\n",
    "    \n",
    "    return theta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exp. Unit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "allLastCounts = TagMap()\n",
    "allLastCountsAvailable = TagMap()\n",
    "allRes = TagMap()\n",
    "allLastCountsInfo = TagMap()\n",
    "\n",
    "lastCounts = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expUnit(catol: float, depth: int):\n",
    "    expectation = expecting(Ising_matrix=Ising_matrix)\n",
    "    res = minimize(\n",
    "        expectation, \n",
    "        thetaGen(depth), \n",
    "        options = {\n",
    "            'iprint': 1, \n",
    "            'disp': False, \n",
    "            'maxiter': 10000,\n",
    "            'catol': 0.002, \n",
    "            'rhobeg': 5.0\n",
    "        },\n",
    "        method='COBYLA'\n",
    "    )\n",
    "    allLastCounts[(f'catol={catol}', f'depth={depth}')].append(lastCounts)\n",
    "    lastFilter = filterCounts(lastCounts)\n",
    "    allLastCountsAvailable[(f'catol={catol}', f'depth={depth}')].append(lastFilter)\n",
    "    \n",
    "    allRes[(f'catol={catol}', f'depth={depth}')].append(res)\n",
    "    allLastCountsInfo[(f'catol={catol}', f'depth={depth}')].append({\n",
    "        'allShots': sum(lastCounts.values()),\n",
    "        'available': sum(lastFilter.values()),\n",
    "        'ratio': sum(lastFilter.values())/sum(lastCounts.values()),\n",
    "    })\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "catolList = [0.0002, 0.002, 0.02, 0.2, 2]\n",
    "depthList = range(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "configList = [\n",
    "    {\n",
    "        'catol': catol, \n",
    "        'depth': depth\n",
    "    }\n",
    "    for catol in catolList\n",
    "    for depth in depthList\n",
    "]\n",
    "\n",
    "# for config in configList:\n",
    "#     expUnit(**config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# allLastCountsAvailable = TagMap()\n",
    "# allLastCountsInfo = TagMap()\n",
    "\n",
    "# def refiliter(catol: float, depth: int):\n",
    "\n",
    "#     lastFilter = filterCounts(allLastCounts[(f'catol={catol}', f'depth={depth}')][0])\n",
    "#     allLastCountsAvailable[(f'catol={catol}', f'depth={depth}')].append(lastFilter)\n",
    "#     allLastCountsInfo[(f'catol={catol}', f'depth={depth}')].append({\n",
    "#         'allShots': sum(lastCounts.values()),\n",
    "#         'available': sum(lastFilter.values()),\n",
    "#         'ratio': sum(lastFilter.values())/sum(lastCounts.values()),\n",
    "#     })\n",
    "\n",
    "\n",
    "# for config in configList:\n",
    "#     refiliter(**config)\n",
    "#     # print(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# expsName = Path('test.3nodes.001')\n",
    "# if not os.path.exists(expsName):\n",
    "#     os.mkdir(expsName)\n",
    "\n",
    "# allLastCounts.export(\n",
    "#     saveLocation=expsName,\n",
    "#     name='allLastCounts'\n",
    "# )\n",
    "# allLastCountsAvailable.export(\n",
    "#     saveLocation=expsName,\n",
    "#     name='allLastCountsAvailable'\n",
    "# )\n",
    "# allRes.export(\n",
    "#     saveLocation=expsName,\n",
    "#     name='allRes'\n",
    "# )\n",
    "# allLastCountsInfo.export(\n",
    "#     saveLocation=expsName,\n",
    "#     name='allLastCountsInfo'\n",
    "# )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "expsName = Path('test.4nodes.001')\n",
    "if not os.path.exists(expsName):\n",
    "    os.mkdir(expsName)\n",
    "\n",
    "allLastCounts = TagMap.read(\n",
    "    saveLocation=expsName,\n",
    "    tagmapName='allLastCounts'\n",
    ")\n",
    "allLastCountsAvailable = TagMap.read(\n",
    "    saveLocation=expsName,\n",
    "    tagmapName='allLastCountsAvailable'\n",
    ")\n",
    "allRes = TagMap.read(\n",
    "    saveLocation=expsName,\n",
    "    tagmapName='allRes'\n",
    ")\n",
    "allLastCountsInfo = TagMap.read(\n",
    "    saveLocation=expsName,\n",
    "    tagmapName='allLastCountsInfo'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0002 0\n",
      "0.0002 1\n",
      "0.0002 2\n",
      "0.0002 3\n",
      "0.0002 4\n",
      "0.0002 5\n",
      "0.0002 6\n",
      "0.0002 7\n",
      "0.0002 8\n",
      "0.002 0\n",
      "0.002 1\n",
      "0.002 2\n",
      "0.002 3\n",
      "0.002 4\n",
      "0.002 5\n",
      "0.002 6\n",
      "0.002 7\n",
      "0.002 8\n",
      "0.02 0\n",
      "0.02 1\n",
      "0.02 2\n",
      "0.02 3\n",
      "0.02 4\n",
      "0.02 5\n",
      "0.02 6\n",
      "0.02 7\n",
      "0.02 8\n",
      "0.2 0\n",
      "0.2 1\n",
      "0.2 2\n",
      "0.2 3\n",
      "0.2 4\n",
      "0.2 5\n",
      "0.2 6\n",
      "0.2 7\n",
      "0.2 8\n",
      "2 0\n",
      "2 1\n",
      "2 2\n",
      "2 3\n",
      "2 4\n",
      "2 5\n",
      "2 6\n",
      "2 7\n",
      "2 8\n"
     ]
    }
   ],
   "source": [
    "allLastCountsAvailable = TagMap()\n",
    "allLastCountsInfo = TagMap()\n",
    "\n",
    "def refiliter(catol: float, depth: int):\n",
    "\n",
    "    lastCounts = allLastCounts[(f'catol={catol}', f'depth={depth}')][0]\n",
    "    lastFilter = filterCounts(lastCounts)\n",
    "    allLastCountsAvailable[(f'catol={catol}', f'depth={depth}')].append(lastFilter)\n",
    "    allLastCountsInfo[(f'catol={catol}', f'depth={depth}')].append({\n",
    "        'allShots': sum(lastCounts.values()),\n",
    "        'available': sum(lastFilter.values()),\n",
    "        'ratio': sum(lastFilter.values())/sum(lastCounts.values()),\n",
    "    })\n",
    "    print(catol, depth)\n",
    "\n",
    "\n",
    "for config in configList:\n",
    "    refiliter(**config)\n",
    "    # print(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1000010000010010': 333}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filterCounts({'1000010000010010': 333})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('test.4nodes.001/test.4nodes.001.allLastCountsInfo.json')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if not os.path.exists(expsName):\n",
    "    os.mkdir(expsName)\n",
    "\n",
    "allLastCounts.export(\n",
    "    saveLocation=expsName,\n",
    "    additionName=expsName,\n",
    "    name='allLastCounts'\n",
    ")\n",
    "allLastCountsAvailable.export(\n",
    "    saveLocation=expsName,\n",
    "    additionName=expsName,\n",
    "    name='allLastCountsAvailable'\n",
    ")\n",
    "allRes.export(\n",
    "    saveLocation=expsName,\n",
    "    additionName=expsName,\n",
    "    name='allRes'\n",
    ")\n",
    "allLastCountsInfo.export(\n",
    "    saveLocation=expsName,\n",
    "    additionName=expsName,\n",
    "    name='allLastCountsInfo'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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