exp-forte.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_braket_provider import to_braket, BraketProvider\n",
    "from qiskit_nature.second_q.operators import FermionicOp\n",
    "from qiskit_nature.second_q.drivers import PySCFDriver\n",
    "from qiskit_nature.units import DistanceUnit\n",
    "from qiskit.quantum_info import Statevector\n",
    "from qiskit_nature.second_q.mappers.fermionic_mapper import FermionicMapper\n",
    "from statistics import mean, variance\n",
    "from utility import Execution\n",
    "\n",
    "from braket.aws import AwsDevice, DirectReservation\n",
    "from braket.devices import Devices\n",
    "from braket.error_mitigation import Debias\n",
    "\n",
    "\n",
    "hamiltonian: FermionicOp = (\n",
    "    PySCFDriver(\n",
    "        atom=\"H 0 0 0; H 0 0 0.735\",\n",
    "        basis=\"sto3g\",\n",
    "        charge=0,\n",
    "        spin=0,\n",
    "        unit=DistanceUnit.ANGSTROM,\n",
    "    )\n",
    "    .run()\n",
    "    .hamiltonian.second_q_op()\n",
    ")\n",
    "\n",
    "# fill in your own reserve arn\n",
    "RESERVE_ARN = \"arn:aws:braket:us-east-1:849837135378:reservation/699fdc53-b71f-43ea-b855-5ab407f3bd5e\"\n",
    "\n",
    "\n",
    "device = AwsDevice(Devices.IonQ.Forte1)\n",
    "reservation = DirectReservation(device, reservation_arn=RESERVE_ARN).start()\n",
    "\n",
    "backend = BraketProvider().get_backend(\"Forte 1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process(data: dict[str, int], mapper: FermionicMapper, hamiltonian):\n",
    "    hamiltonian = mapper.map(hamiltonian)\n",
    "\n",
    "    energies = []\n",
    "\n",
    "    for state, n in data.items():\n",
    "        initial_state = Statevector.from_label(state[::-1])\n",
    "        exp_energy = initial_state.expectation_value(hamiltonian)  # type: ignore\n",
    "\n",
    "        for _ in range(n):\n",
    "            energies.append(exp_energy.real)\n",
    "\n",
    "    return (\n",
    "        mean(energies),\n",
    "        variance(energies),\n",
    "    )\n",
    "\n",
    "\n",
    "def run_on_braket(hamiltonian: FermionicOp, mapper: FermionicMapper):\n",
    "    simulation = Execution(hamiltonian, mapper)\n",
    "    simulation.circuit.global_phase = 0\n",
    "    task = backend.run(simulation.circuit, shots=1500, device_parameters={\"errorMitigation\": Debias()})\n",
    "    return task, mapper, hamiltonian  # type: ignore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "tasks = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jordan-Wigner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamiltonian simulation:\n",
      "  Solve ground state:\n",
      "    Ground energy = -1.857275030202377\n",
      "  Construct and transpile Pauli evolution:\n",
      "    Time duration    = 1.0\n",
      "    Basis gates      = cx, u3\n",
      "    Synthesis method = LieTrotter\n",
      "  Circuit summary:\n",
      "    Depth = 89\n",
      "    Gates = 57 U3, 50 CX, 4 MEASURE\n"
     ]
    }
   ],
   "source": [
    "from qiskit_nature.second_q.mappers import JordanWignerMapper\n",
    "\n",
    "tasks.append(run_on_braket(hamiltonian, JordanWignerMapper()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(<qiskit_braket_provider.providers.braket_quantum_task.BraketQuantumTask at 0x7f6ad9acabc0>,\n",
       "  <qiskit_nature.second_q.mappers.jordan_wigner_mapper.JordanWignerMapper at 0x7f6d1c5d6c20>,\n",
       "  FermionicOp({'+_0 +_0 -_0 -_0': 0.33785507740175813, '+_0 +_1 -_1 -_0': 0.3322908651276482, '+_0 +_2 -_2 -_0': 0.33785507740175813, '+_0 +_3 -_3 -_0': 0.3322908651276482, '+_0 +_0 -_1 -_1': 0.09046559989211572, '+_0 +_1 -_0 -_1': 0.09046559989211572, '+_0 +_2 -_3 -_1': 0.09046559989211572, '+_0 +_3 -_2 -_1': 0.09046559989211572, '+_1 +_0 -_1 -_0': 0.09046559989211572, '+_1 +_1 -_0 -_0': 0.09046559989211572, '+_1 +_2 -_3 -_0': 0.09046559989211572, '+_1 +_3 -_2 -_0': 0.09046559989211572, '+_1 +_0 -_0 -_1': 0.3322908651276482, '+_1 +_1 -_1 -_1': 0.34928686136600884, '+_1 +_2 -_2 -_1': 0.3322908651276482, '+_1 +_3 -_3 -_1': 0.34928686136600884, '+_2 +_0 -_0 -_2': 0.33785507740175813, '+_2 +_1 -_1 -_2': 0.3322908651276482, '+_2 +_2 -_2 -_2': 0.33785507740175813, '+_2 +_3 -_3 -_2': 0.3322908651276482, '+_2 +_0 -_1 -_3': 0.09046559989211572, '+_2 +_1 -_0 -_3': 0.09046559989211572, '+_2 +_2 -_3 -_3': 0.09046559989211572, '+_2 +_3 -_2 -_3': 0.09046559989211572, '+_3 +_0 -_1 -_2': 0.09046559989211572, '+_3 +_1 -_0 -_2': 0.09046559989211572, '+_3 +_2 -_3 -_2': 0.09046559989211572, '+_3 +_3 -_2 -_2': 0.09046559989211572, '+_3 +_0 -_0 -_3': 0.3322908651276482, '+_3 +_1 -_1 -_3': 0.34928686136600884, '+_3 +_2 -_2 -_3': 0.3322908651276482, '+_3 +_3 -_3 -_3': 0.34928686136600884, '+_0 -_0': -1.25633907300325, '+_1 -_1': -0.47189600728114184, '+_2 -_2': -1.25633907300325, '+_3 -_3': -0.47189600728114184}, num_spin_orbitals=4, ))]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bravyi-Kitaev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamiltonian simulation:\n",
      "  Solve ground state:\n",
      "    Ground energy = -1.857275030202382\n",
      "  Construct and transpile Pauli evolution:\n",
      "    Time duration    = 1.0\n",
      "    Basis gates      = cx, u3\n",
      "    Synthesis method = LieTrotter\n",
      "  Circuit summary:\n",
      "    Depth = 88\n",
      "    Gates = 52 CX, 48 U3, 4 MEASURE\n"
     ]
    }
   ],
   "source": [
    "from qiskit_nature.second_q.mappers import BravyiKitaevMapper\n",
    "\n",
    "tasks.append(run_on_braket(hamiltonian, BravyiKitaevMapper()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Balanced Ternary Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamiltonian simulation:\n",
      "  Solve ground state:\n",
      "    Ground energy = -1.8572750302023757\n",
      "  Construct and transpile Pauli evolution:\n",
      "    Time duration    = 1.0\n",
      "    Basis gates      = cx, u3\n",
      "    Synthesis method = LieTrotter\n",
      "  Circuit summary:\n",
      "    Depth = 88\n",
      "    Gates = 55 U3, 50 CX, 4 MEASURE\n"
     ]
    }
   ],
   "source": [
    "from ternary_tree_mapper import TernaryTreeMapper\n",
    "\n",
    "tasks.append(run_on_braket(hamiltonian, TernaryTreeMapper(pair=True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fermihedral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamiltonian simulation:\n",
      "  Solve ground state:\n",
      "    Ground energy = -1.8572750302023757\n",
      "  Construct and transpile Pauli evolution:\n",
      "    Time duration    = 1.0\n",
      "    Basis gates      = cx, u3\n",
      "    Synthesis method = LieTrotter\n",
      "  Circuit summary:\n",
      "    Depth = 77\n",
      "    Gates = 45 U3, 45 CX, 4 MEASURE\n"
     ]
    }
   ],
   "source": [
    "from utility import FermihedralMapper\n",
    "\n",
    "tasks.append(run_on_braket(hamiltonian, FermihedralMapper.molecule()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Our Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                0/6 [00:00<?, ?it/s]]\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamiltonian simulation:\n",
      "  Solve ground state:\n",
      "    Ground energy = -1.8572750302023784\n",
      "  Construct and transpile Pauli evolution:\n",
      "    Time duration    = 1.0\n",
      "    Basis gates      = cx, u3\n",
      "    Synthesis method = LieTrotter\n",
      "  Circuit summary:\n",
      "    Depth = 78\n",
      "    Gates = 57 U3, 42 CX, 4 MEASURE\n"
     ]
    }
   ],
   "source": [
    "from ternary_bonsai_mapper import HamiltonianTernaryBonsaiMapper\n",
    "\n",
    "tasks.append(run_on_braket(hamiltonian, HamiltonianTernaryBonsaiMapper(hamiltonian)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit_nature.second_q.mappers.fermionic_mapper import FermionicMapper\n",
    "from qiskit_nature.second_q.drivers import PySCFDriver\n",
    "from qiskit_nature.units import DistanceUnit\n",
    "from qiskit_nature.second_q.operators import FermionicOp\n",
    "from braket.aws.aws_quantum_task import AwsQuantumTask\n",
    "from qiskit.quantum_info import Statevector\n",
    "from statistics import mean, variance\n",
    "\n",
    "def process(job : AwsQuantumTask, mapper: FermionicMapper, hamiltonian):\n",
    "    data = job._download_result().measurement_counts # type: ignore\n",
    "\n",
    "    hamiltonian = mapper.map(hamiltonian)\n",
    "\n",
    "    energies = []\n",
    "\n",
    "    for state, n in data.items():\n",
    "        initial_state = Statevector.from_label(state[::-1])\n",
    "        exp_energy = initial_state.expectation_value(hamiltonian)  # type: ignore\n",
    "\n",
    "        for _ in range(n):\n",
    "            energies.append(exp_energy.real)\n",
    "\n",
    "    return (\n",
    "        mean(energies),\n",
    "        variance(energies),\n",
    "    )\n",
    "\n",
    "hamiltonian: FermionicOp = (\n",
    "    PySCFDriver(\n",
    "        atom=\"H 0 0 0; H 0 0 0.735\",\n",
    "        basis=\"sto3g\",\n",
    "        charge=0,\n",
    "        spin=0,\n",
    "        unit=DistanceUnit.ANGSTROM,\n",
    "    )\n",
    "    .run()\n",
    "    .hamiltonian.second_q_op()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'hamiltonian' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m fhtask \u001b[38;5;241m=\u001b[39m AwsQuantumTask(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marn:aws:braket:us-east-1:849837135378:quantum-task/f4b13f36-5583-472f-a19e-e2c922db6eb7\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     11\u001b[0m hatttask \u001b[38;5;241m=\u001b[39m AwsQuantumTask(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marn:aws:braket:us-east-1:849837135378:quantum-task/efe8ff7b-1f4e-45ed-a126-dfce20d0407d\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 13\u001b[0m tasks \u001b[38;5;241m=\u001b[39m [(jwtask, JordanWignerMapper()), (bktask, BravyiKitaevMapper()), (btttask, TernaryTreeMapper(pair\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)), (fhtask, FermihedralMapper\u001b[38;5;241m.\u001b[39mmolecule()), (hatttask, HamiltonianTernaryBonsaiMapper(\u001b[43mhamiltonian\u001b[49m))]\n",
      "\u001b[0;31mNameError\u001b[0m: name 'hamiltonian' is not defined"
     ]
    }
   ],
   "source": [
    "from braket.aws.aws_quantum_task import AwsQuantumTask\n",
    "from qiskit_nature.second_q.mappers import JordanWignerMapper, BravyiKitaevMapper\n",
    "from ternary_bonsai_mapper import HamiltonianTernaryBonsaiMapper\n",
    "from ternary_tree_mapper import TernaryTreeMapper\n",
    "from utility import FermihedralMapper\n",
    "\n",
    "jwtask = AwsQuantumTask(\"arn:aws:braket:us-east-1:849837135378:quantum-task/af4dc72c-3546-4ed3-abe9-fda167e9aecb\")\n",
    "bktask = AwsQuantumTask(\"arn:aws:braket:us-east-1:849837135378:quantum-task/bd373783-52a9-4000-8b29-0b2ed08abb33\")\n",
    "btttask = AwsQuantumTask(\"arn:aws:braket:us-east-1:849837135378:quantum-task/577a7833-ac65-4145-b2d2-a2a0b1f873eb\")\n",
    "fhtask = AwsQuantumTask(\"arn:aws:braket:us-east-1:849837135378:quantum-task/f4b13f36-5583-472f-a19e-e2c922db6eb7\")\n",
    "hatttask = AwsQuantumTask(\"arn:aws:braket:us-east-1:849837135378:quantum-task/efe8ff7b-1f4e-45ed-a126-dfce20d0407d\")\n",
    "\n",
    "tasks = [(jwtask, JordanWignerMapper()), (bktask, BravyiKitaevMapper()), (btttask, TernaryTreeMapper(pair=True)), (fhtask, FermihedralMapper.molecule()), (hatttask, HamiltonianTernaryBonsaiMapper(hamiltonian))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Task is in terminal state CANCELLED and no result is available.\n",
      "Task is in terminal state CANCELLED and no result is available.\n",
      "Task is in terminal state CANCELLED and no result is available.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.rc(\"font\", size=34, family=\"serif\", serif=\"cmr10\")\n",
    "plt.rcParams['axes.formatter.use_mathtext'] = True\n",
    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
    "\n",
    "THEO = -1.857275030202381\n",
    "\n",
    "\n",
    "plt.figure(figsize=(14, 4), layout=\"constrained\")\n",
    "for n, (mapper, (t, m)) in enumerate(zip((\"JW\", \"BK\", \"BTT\", \"FH\", \"HATT\"), tasks)):\n",
    "    exp, var = process(t, m, hamiltonian)\n",
    "    plt.plot([n - 0.3, n + 0.3], [exp, exp], color=\"C3\", linewidth=5)\n",
    "    plt.text(n - 0.46, exp + 0.03, f\"{exp:.3f}\", color=\"C3\", fontsize=32)\n",
    "    plt.errorbar(n, exp, var, color=\"black\", linewidth=3)\n",
    "    plt.text(n + 0.05, exp - var, f\"{var:.3f}\", fontsize=32)\n",
    "\n",
    "plt.xlim(0 - 0.5, 4 + 0.5)\n",
    "plt.ylim(THEO - 0.3, -0.9)\n",
    "plt.yticks([-2.0, -1.6, -1.2])\n",
    "plt.ylabel(\"Energy\")\n",
    "plt.xticks([0, 1, 2, 3, 4], [\"JW\", \"BK\", \"BTT\", \"FH\", \"HATT\"], fontfamily=\"sans serif\")\n",
    "\n",
    "plt.plot([-1, 5], [THEO, THEO], linewidth=3)\n",
    "plt.annotate(f\"THEORETICAL={THEO:.3f}\", (1.8, THEO - 0.01), (2, THEO - 0.28), arrowprops=dict(facecolor='C0', edgecolor=\"C0\", shrink=0.05, width=2), fontfamily=\"monospace\", color=\"C0\")\n",
    "\n",
    "plt.savefig(\"tests/realsystem.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}