{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "12bbcb1f-f2f5-471b-9c8e-f8d46cfb8479",
   "metadata": {},
   "source": [
    "# EstimatorV2 Usage\n",
    "### Modified for Quantum Rings toolkit for Qiskit 2.x\n",
    "#### See here: https://qiskit.qotlabs.org/docs/guides/primitives-examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a93bbe11-8439-454e-b979-d3618fea1d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "# Setup your account\n",
    "# You can also save your account locally using the class method QrRuntimeService.save_account(...) and\n",
    "# invoke the QrRuntimeService class constructor without any arguments.\n",
    "#\n",
    "\n",
    "import os\n",
    "\n",
    "my_token = os.environ[\"QR_TOKEN\"]\n",
    "my_name = os.environ[\"QR_ACCOUNT\"]\n",
    "\n",
    "#\n",
    "# Set the backend of your choice, depending upon the task and your hardware configuration.\n",
    "# See SDK documentation for additional help.\n",
    "#\n",
    "\n",
    "my_backend = \"scarlet_quantum_rings\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a99345-6ebe-422f-91c5-dd3979a72c8f",
   "metadata": {},
   "source": [
    "## Basic Estimator Usage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "682f7d6e-84bd-49a7-a2ca-b59724dcb4a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['scarlet_quantum_rings']\n",
      ">>> Expectation value: -1.0636533500290943\n"
     ]
    }
   ],
   "source": [
    "from dataclasses import asdict\n",
    "#from qiskit_ibm_runtime import QiskitRuntimeService\n",
    "#from qiskit_ibm_runtime import EstimatorV2 as Estimator\n",
    " \n",
    "#service = QiskitRuntimeService()\n",
    "#backend = service.least_busy(operational=True, simulator=False)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrRuntimeService\n",
    "\n",
    "service = QrRuntimeService( token = my_token , name = my_name)\n",
    "avaiable_backends = service.backends()\n",
    "print(avaiable_backends)\n",
    "#backend = service.least_busy(operational=True, simulator=False)\n",
    "backend = service.backend(name = my_backend, precision = \"double\", gpu = 0, num_qubits = 12)\n",
    "\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    " \n",
    "# Step 1: Define operator\n",
    "op = SparsePauliOp.from_list(\n",
    "    [\n",
    "        (\"II\", -1.052373245772859),\n",
    "        (\"IZ\", 0.39793742484318045),\n",
    "        (\"ZI\", -0.39793742484318045),\n",
    "        (\"ZZ\", -0.01128010425623538),\n",
    "        (\"XX\", 0.18093119978423156),\n",
    "    ]\n",
    ")\n",
    " \n",
    "# Step 2: Define quantum state\n",
    "circuit = QuantumCircuit(2)\n",
    "circuit.x(0)\n",
    "circuit.x(1)\n",
    "\n",
    "from qiskit.transpiler.preset_passmanagers import generate_preset_pass_manager\n",
    " \n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
    "isa_circuit = pm.run(circuit)\n",
    " \n",
    "isa_observable = op.apply_layout(isa_circuit.layout)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrEstimatorV2 as Estimator\n",
    "estimator = Estimator(backend = backend)\n",
    "job = estimator.run([(isa_circuit, isa_observable)])\n",
    " \n",
    "# Get results for the first (and only) PUB\n",
    "pub_result = job.result()[0]\n",
    "\n",
    "#expect: [-0.8879899326312926]\n",
    "print(f\">>> Expectation value: {pub_result.data.evs[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "beb9565b-2a11-4eb6-8578-306646f39bbe",
   "metadata": {},
   "source": [
    "## Run a Single Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20650cc8-0c5d-44d5-93b4-1385529ff6d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " > Expectation value: 0.7071066498756409\n",
      " > Metadata: {'target_precision': 0.001, 'shots': 1, 'circuit_metadata': {}}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from qiskit.circuit.library import iqp\n",
    "from qiskit.transpiler import generate_preset_pass_manager\n",
    "from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
    "#from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
    " \n",
    "n_qubits = 5 #0\n",
    " \n",
    "#service = QiskitRuntimeService()\n",
    "#backend = service.least_busy(\n",
    "#    operational=True, simulator=False, min_num_qubits=n_qubits\n",
    "#)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrRuntimeService\n",
    "from quantumrings.toolkit.qiskit import QrEstimatorV2 as Estimator\n",
    "\n",
    "service = QrRuntimeService( token = my_token , name = my_name)\n",
    "backend = service.backend(name = my_backend, precision = \"single\", gpu = 0, num_qubits = n_qubits)\n",
    "\n",
    "mat = np.real(random_hermitian(n_qubits, seed=1234))\n",
    "circuit = iqp(mat)\n",
    "observable = SparsePauliOp(\"Z\" * n_qubits)\n",
    " \n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
    "isa_circuit = pm.run(circuit)\n",
    "isa_observable = observable.apply_layout(isa_circuit.layout)\n",
    " \n",
    "estimator = Estimator(mode=backend)\n",
    "job = estimator.run([(isa_circuit, isa_observable)])\n",
    "result = job.result()\n",
    " \n",
    "print(f\" > Expectation value: {result[0].data.evs[0]}\")\n",
    "print(f\" > Metadata: {result[0].metadata}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ee521c-fad7-4e15-a941-911614cfbf6a",
   "metadata": {},
   "source": [
    "## Run multiple experiments in a single job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a29fbd3-d4ce-4c9f-b18d-c70e4afb0756",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> Expectation values for PUB 0: 0.0\n",
      ">>> Standard errors for PUB 0: [0.]\n",
      ">>> Expectation values for PUB 1: 0.0\n",
      ">>> Standard errors for PUB 1: [0.]\n",
      ">>> Expectation values for PUB 2: 0.0\n",
      ">>> Standard errors for PUB 2: [0.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from qiskit.circuit.library import iqp\n",
    "from qiskit.transpiler import generate_preset_pass_manager\n",
    "from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
    "#from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
    " \n",
    "n_qubits = 5 #0\n",
    " \n",
    "#service = QiskitRuntimeService()\n",
    "#backend = service.least_busy(\n",
    "#    operational=True, simulator=False, min_num_qubits=n_qubits\n",
    "#)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrRuntimeService\n",
    "from quantumrings.toolkit.qiskit import QrEstimatorV2 as Estimator\n",
    "\n",
    "service = QrRuntimeService( token = my_token , name = my_name)\n",
    "backend = service.backend(name = my_backend, precision = \"single\", gpu = 0, num_qubits = n_qubits)\n",
    "\n",
    "rng = np.random.default_rng()\n",
    "mats = [np.real(random_hermitian(n_qubits, seed=rng)) for _ in range(3)]\n",
    " \n",
    "pubs = []\n",
    "circuits = [iqp(mat) for mat in mats]\n",
    "observables = [\n",
    "    SparsePauliOp(\"X\" * n_qubits),\n",
    "    SparsePauliOp(\"Y\" * n_qubits),\n",
    "    SparsePauliOp(\"Z\" * n_qubits),\n",
    "]\n",
    " \n",
    "# Get ISA circuits\n",
    "pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
    " \n",
    "for qc, obs in zip(circuits, observables):\n",
    "    isa_circuit = pm.run(qc)\n",
    "    isa_obs = obs.apply_layout(isa_circuit.layout)\n",
    "    pubs.append((isa_circuit, isa_obs))\n",
    " \n",
    "estimator = Estimator(backend=backend)\n",
    "job = estimator.run(pubs)\n",
    "job_result = job.result()\n",
    " \n",
    "for idx in range(len(pubs)):\n",
    "    pub_result = job_result[idx]\n",
    "    print(f\">>> Expectation values for PUB {idx}: {pub_result.data.evs[0]}\")\n",
    "    print(f\">>> Standard errors for PUB {idx}: {pub_result.data.stds}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40113849-aba2-4797-b09f-267c87a59578",
   "metadata": {},
   "source": [
    "## Run parameterized circuits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e797bfc-4341-46d3-90dd-2e8b5066fc2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reshaped_ops: [[SparsePauliOp(['ZZ'],\n",
      "                coeffs=[1.+0.j])]\n",
      " [SparsePauliOp(['ZX'],\n",
      "                coeffs=[1.+0.j])]\n",
      " [SparsePauliOp(['XZ'],\n",
      "                coeffs=[1.+0.j])]\n",
      " [SparsePauliOp(['XX'],\n",
      "                coeffs=[1.+0.j])]]\n",
      ">>> Expectation values: [[ 1.00000012e+00  9.51056659e-01  8.09017062e-01  5.87785363e-01\n",
      "   3.09017062e-01  0.00000000e+00 -3.09017062e-01 -5.87785363e-01\n",
      "  -8.09017181e-01 -9.51056659e-01 -1.00000012e+00 -9.51056659e-01\n",
      "  -8.09017003e-01 -5.87785065e-01 -3.09017181e-01  0.00000000e+00\n",
      "   3.09017211e-01  5.87785125e-01  8.09017003e-01  9.51056659e-01\n",
      "   1.00000012e+00]\n",
      " [ 0.00000000e+00  3.09017062e-01  5.87785363e-01  8.09017181e-01\n",
      "   9.51056659e-01  1.00000000e+00  9.51056659e-01  8.09017181e-01\n",
      "   5.87785363e-01  3.09017122e-01 -8.74227837e-08 -3.09017003e-01\n",
      "  -5.87785363e-01 -8.09017241e-01 -9.51056540e-01 -1.00000000e+00\n",
      "  -9.51056600e-01 -8.09017181e-01 -5.87785363e-01 -3.09017003e-01\n",
      "   0.00000000e+00]\n",
      " [ 0.00000000e+00 -3.09017062e-01 -5.87785363e-01 -8.09017181e-01\n",
      "  -9.51056659e-01 -1.00000000e+00 -9.51056659e-01 -8.09017181e-01\n",
      "  -5.87785363e-01 -3.09017122e-01  8.74227837e-08  3.09017003e-01\n",
      "   5.87785363e-01  8.09017241e-01  9.51056540e-01  1.00000000e+00\n",
      "   9.51056600e-01  8.09017181e-01  5.87785363e-01  3.09017003e-01\n",
      "   0.00000000e+00]\n",
      " [ 1.00000012e+00  9.51056659e-01  8.09017062e-01  5.87785363e-01\n",
      "   3.09017062e-01  0.00000000e+00 -3.09017062e-01 -5.87785363e-01\n",
      "  -8.09017181e-01 -9.51056659e-01 -1.00000012e+00 -9.51056659e-01\n",
      "  -8.09017003e-01 -5.87785065e-01 -3.09017181e-01  0.00000000e+00\n",
      "   3.09017211e-01  5.87785125e-01  8.09017003e-01  9.51056659e-01\n",
      "   1.00000012e+00]]\n",
      ">>> Standard errors: [[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
      ">>> Metadata: {'target_precision': 0.001, 'shots': 1, 'circuit_metadata': {}}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "from qiskit.circuit import QuantumCircuit, Parameter\n",
    "from qiskit.quantum_info import SparsePauliOp\n",
    "from qiskit.transpiler import generate_preset_pass_manager\n",
    "#from qiskit_ibm_runtime import QiskitRuntimeService, EstimatorV2 as Estimator\n",
    " \n",
    "#service = QiskitRuntimeService()\n",
    "#backend = service.least_busy(operational=True, simulator=False)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrRuntimeService\n",
    "from quantumrings.toolkit.qiskit import QrEstimatorV2 as Estimator\n",
    "\n",
    "service = QrRuntimeService( token = my_token , name = my_name)\n",
    "backend = service.backend(name = my_backend, precision = \"single\", gpu = 0, num_qubits = 2)\n",
    " \n",
    "# Step 1: Map classical inputs to a quantum problem\n",
    "theta = Parameter(\"θ\")\n",
    " \n",
    "chsh_circuit = QuantumCircuit(2)\n",
    "chsh_circuit.h(0)\n",
    "chsh_circuit.cx(0, 1)\n",
    "chsh_circuit.ry(theta, 0)\n",
    " \n",
    "number_of_phases = 21\n",
    "phases = np.linspace(0, 2 * np.pi, number_of_phases)\n",
    "individual_phases = [[ph] for ph in phases]\n",
    " \n",
    "ZZ = SparsePauliOp.from_list([(\"ZZ\", 1)])\n",
    "ZX = SparsePauliOp.from_list([(\"ZX\", 1)])\n",
    "XZ = SparsePauliOp.from_list([(\"XZ\", 1)])\n",
    "XX = SparsePauliOp.from_list([(\"XX\", 1)])\n",
    "ops = [ZZ, ZX, XZ, XX]\n",
    " \n",
    "# Step 2: Optimize problem for quantum execution.\n",
    " \n",
    "pm = generate_preset_pass_manager(backend=backend, optimization_level=1)\n",
    "chsh_isa_circuit = pm.run(chsh_circuit)\n",
    "isa_observables = [\n",
    "    operator.apply_layout(chsh_isa_circuit.layout) for operator in ops\n",
    "]\n",
    " \n",
    "# Step 3: Execute using Qiskit primitives.\n",
    " \n",
    "# Reshape observable array for broadcasting\n",
    "reshaped_ops = np.fromiter(isa_observables, dtype=object)\n",
    "reshaped_ops = reshaped_ops.reshape((4, 1))\n",
    "\n",
    "print (f\"reshaped_ops: {reshaped_ops}\")\n",
    "\n",
    "estimator = Estimator(backend=backend, options={\"default_shots\": int(1e4)})\n",
    "job = estimator.run([(chsh_isa_circuit, reshaped_ops, individual_phases)])\n",
    "# Get results for the first (and only) PUB\n",
    "pub_result = job.result()[0]\n",
    "print(f\">>> Expectation values: {pub_result.data.evs}\")\n",
    "print(f\">>> Standard errors: {pub_result.data.stds}\")\n",
    "print(f\">>> Metadata: {pub_result.metadata}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9c8f72-9cb5-4420-a324-30d9540d686f",
   "metadata": {},
   "source": [
    "## Use sessions and advanced options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25ecbbe8-6505-4fcb-a63c-f38b9b08b8d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " > Expectation value: 0.0\n",
      " > Metadata: {'target_precision': 0.001, 'shots': 1, 'circuit_metadata': {}}\n",
      " > Another Expectation value: 0.0\n",
      " > More Metadata: {'target_precision': 0.001, 'shots': 1, 'circuit_metadata': {}}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from qiskit.circuit.library import iqp\n",
    "from qiskit.transpiler import generate_preset_pass_manager\n",
    "from qiskit.quantum_info import SparsePauliOp, random_hermitian\n",
    "#from qiskit_ibm_runtime import (\n",
    "#    QiskitRuntimeService,\n",
    "#    Session,\n",
    "#    EstimatorV2 as Estimator,\n",
    "#)\n",
    " \n",
    "n_qubits = 5 #0\n",
    " \n",
    "#service = QiskitRuntimeService()\n",
    "#backend = service.least_busy(\n",
    "#    operational=True, simulator=False, min_num_qubits=n_qubits\n",
    "#)\n",
    "\n",
    "from quantumrings.toolkit.qiskit import QrRuntimeService\n",
    "from quantumrings.toolkit.qiskit import QrEstimatorV2 as Estimator\n",
    "from quantumrings.toolkit.qiskit import QrSession as Session\n",
    "\n",
    "service = QrRuntimeService( token = my_token , name = my_name)\n",
    "backend = service.backend(name = my_backend, precision = \"single\", gpu = 0, num_qubits = n_qubits)\n",
    " \n",
    "rng = np.random.default_rng(1234)\n",
    "mat = np.real(random_hermitian(n_qubits, seed=rng))\n",
    "circuit = iqp(mat)\n",
    "mat = np.real(random_hermitian(n_qubits, seed=rng))\n",
    "another_circuit = iqp(mat)\n",
    "observable = SparsePauliOp(\"X\" * n_qubits)\n",
    "another_observable = SparsePauliOp(\"Y\" * n_qubits)\n",
    " \n",
    "pm = generate_preset_pass_manager(optimization_level=1, backend=backend)\n",
    "isa_circuit = pm.run(circuit)\n",
    "another_isa_circuit = pm.run(another_circuit)\n",
    "isa_observable = observable.apply_layout(isa_circuit.layout)\n",
    "another_isa_observable = another_observable.apply_layout(\n",
    "    another_isa_circuit.layout\n",
    ")\n",
    " \n",
    "with Session(backend=backend) as session:\n",
    "    estimator = Estimator(mode=session)\n",
    " \n",
    "    estimator.options.resilience_level = 1\n",
    " \n",
    "    job = estimator.run([(isa_circuit, isa_observable)])\n",
    "    another_job = estimator.run(\n",
    "        [(another_isa_circuit, another_isa_observable)]\n",
    "    )\n",
    "    result = job.result()\n",
    "    another_result = another_job.result()\n",
    " \n",
    "    # first job\n",
    "    print(f\" > Expectation value: {result[0].data.evs[0]}\")\n",
    "    print(f\" > Metadata: {result[0].metadata}\")\n",
    " \n",
    "    # second job\n",
    "    print(f\" > Another Expectation value: {another_result[0].data.evs[0]}\")\n",
    "    print(f\" > More Metadata: {another_result[0].metadata}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4eca7de-a529-416f-85e8-953715d6f4b6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "878099d5-332f-4d2a-bc1d-db71bcc2d57e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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