diff --git a/examples/hydrogen_qiskit.ipynb b/examples/hydrogen_qiskit.ipynb new file mode 100644 index 00000000..83a79693 --- /dev/null +++ b/examples/hydrogen_qiskit.ipynb @@ -0,0 +1,703 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": "# Hydrogen algorithm for QAL Quantum Inspire Workshop on Hybrid Computing ", + "id": "b48c3683d38cabf" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "This notebook describes running a hybrid quantum classical algorith to calculate the electronic ground state energy of a hydrogen molecule on the Quantum Inspire platform.\n", + "\n", + "Part of the cells in the notebook are to be executed. Another part are to be copied to another file." + ], + "id": "e7de03d20a964a8" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Logging in to the Quantum Inspire platform\n", + "\n", + "We start by logging on to the platform" + ], + "id": "1fe5e30c745fb8c1" + }, + { + "metadata": {}, + "cell_type": "code", + "source": "!poetry run qi login https://api.qi2.quantum-inspire.com", + "id": "5e712470eefc6d6a", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Setting up the algorithm Python file\n", + "\n", + "We make a new empty Python file to hold the algorithm." + ], + "id": "66d29b62f486f266" + }, + { + "cell_type": "code", + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "source": [ + "from pathlib import Path\n", + "\n", + "filename = str(Path.cwd() / \"hydrogen_algorithm.py\") \n", + "!touch {filename}" + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Add an `execute` and a `finalize` function to the file with the following input arguments:", + "id": "9e06c3bfbc525b76" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from typing import Any\n", + "from quantuminspire.util.api.quantum_interface import QuantumInterface\n", + "\n", + "\n", + "def execute(qi: QuantumInterface) -> None:\n", + " ...\n", + "\n", + "def finalize(results: Any) -> dict[str, Any]:\n", + " ..." + ], + "id": "8daaf3f52f28f96e", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Have the `execute` function add something to the results, and have `finalize` return them. The `try ... except` clause gives a return message in case of an exception.", + "id": "6614ddb463e43d1c" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from typing import Any\n", + "from quantuminspire.util.api.quantum_interface import QuantumInterface\n", + "import traceback\n", + "\n", + "global_results = []\n", + "\n", + "def execute(qi: QuantumInterface) -> None:\n", + " try:\n", + " result_dict = {\"var\": 2}\n", + " global_results.append(result_dict)\n", + " except Exception:\n", + " global_results.append({\"exception\": traceback.format_exc()})\n", + "\n", + "def finalize(results: Any) -> dict[str, Any]:\n", + " return {\"results\": global_results}" + ], + "id": "5999967ad19c277d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Use the `print_output` function to execute the Python file and print its output. For debugging, you can change where the execution occurs by setting `Config.run_local = True`.", + "id": "bc44c6b00b985cd7" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "class Config:\n", + " run_local = False" + ], + "id": "3c91d6c0fda3a8a7", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import time\n", + "\n", + "def get_output():\n", + " if Config.run_local:\n", + " output = !poetry run qi files run {filename}\n", + " else:\n", + " upload_output = !poetry run qi files upload {filename} 3\n", + " job_id = upload_output[-1].split()[-1]\n", + " while True:\n", + " results_output = !poetry run qi final_results get {job_id}\n", + " output = results_output[-1]\n", + " if output == \"No final results.\":\n", + " print(\".\", end=\"\")\n", + " else:\n", + " print()\n", + " break\n", + " \n", + " time.sleep(1)\n", + " return output\n", + "\n", + "def print_output():\n", + " output = get_output()\n", + " print(output)\n", + " \n", + "print_output()" + ], + "id": "4031bb12fe6b4615", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Adding the hydrogen algorithm\n", + "\n", + "In the next couple of cells, we will be adding the hydrogen algorithm to the `execute` function. Add these commands to the function, inside the `try ... except`\n", + "\n", + "Specify a molecule configuration with the distance between the atoms. Make a molecule driver from this, and use it to generate an ElectronicStructureProblem.\n", + "\n", + "If you want to run the algorithm locally on a Windows computer, see the end of the notebook for instructions." + ], + "id": "1d27bafa8e78106a" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit_nature.second_q.drivers import PySCFDriver\n", + "\n", + "distance = 0.735\n", + "molecule = f\"H 0.0 0.0 0.0; H 0.0 0.0 {distance}\"\n", + "driver = PySCFDriver(molecule)\n", + "es_problem = driver.run()" + ], + "id": "b38435ca858dbfdc", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Get the fermionic (electronic creation and annihilation) operators and store some useful attributes for later ", + "id": "251ab265f723f139" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "fermionic_op = es_problem.hamiltonian.second_q_op()\n", + "n_particles = es_problem.num_particles\n", + "n_spatial_orbitals = es_problem.num_spatial_orbitals\n", + "nuclear_repulsion_energy = es_problem.nuclear_repulsion_energy" + ], + "id": "477e8407a0431666", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Map the fermionic (electronic) operators for the Hamiltonian onto qubit operators:", + "id": "6c7e16c0f2820b49" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit_nature.second_q.mappers import ParityMapper\n", + "\n", + "mapper = ParityMapper(num_particles=(1, 1))\n", + "qubit_op = mapper.map(fermionic_op)" + ], + "id": "fc33cb8697944cfc", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Make the ansatz from the initial state, using the same mapper:", + "id": "a1bf4fee6fd38098" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit_nature.second_q.circuit.library import HartreeFock, UCCSD\n", + "\n", + "initial_state = HartreeFock(n_spatial_orbitals, n_particles, mapper)\n", + "ansatz = UCCSD(n_spatial_orbitals, n_particles, mapper, initial_state=initial_state)" + ], + "id": "1d3a53b494fc14f4", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Use Quantum Inspire's Qiskit backend to make the estimator:", + "id": "7b158797c551827a" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit.primitives import BackendEstimator\n", + "from quantuminspire.sdk.qiskit.backend import QuantumInspireBackend\n", + "\n", + "backend = QuantumInspireBackend(qi) # qi is passed into `execute`\n", + "estimator = BackendEstimator(backend=backend)" + ], + "id": "1fb2c8afaac72b5d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Make the optimizer:", + "id": "4239c37767049c0b" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit_algorithms.optimizers import COBYLA\n", + "\n", + "optimizer = COBYLA(maxiter=1)" + ], + "id": "728112b2b33aa466", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Put everything together to make the VQE algorithm, and run it to get the result:", + "id": "31c9e48ce16f2142" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit_algorithms import VQE\n", + "\n", + "algo = VQE(estimator, ansatz, optimizer)\n", + "result = algo.compute_minimum_eigenvalue(qubit_op)" + ], + "id": "21c37beed3723f1c", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Replace the returned result with the distance and the total energy. The total energy is the sum of the eigenvalue calculated by the algorithm and the nuclear repulsion energy. ", + "id": "5632d24632222883" + }, + { + "metadata": {}, + "cell_type": "code", + "source": "result_dict = {\"distance\": distance, \"total_energy\": result.eigenvalue + nuclear_repulsion_energy}", + "id": "1748e8c980182c56", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Print the output to show the distance and the total energy", + "id": "4cbcdab535882a1c" + }, + { + "metadata": {}, + "cell_type": "code", + "source": "print_output()", + "id": "dcee350bc09a7064", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Varying the distance between the atoms\n", + "\n", + "Now that we can calculate the total energy for a single distance, let's vary the distance and see what happens.\n", + "\n", + "Wrap the entire body of the execute function in a for loop to loop over different distances. \n", + "\n", + "Don't forget to replace the constant `distance`." + ], + "id": "a068b97588e58d8b" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "def execute(qi: QuantumInterface) -> None:\n", + " try:\n", + " distances = np.arange(0.3, 2.5, 0.1)\n", + " results = []\n", + " for distance in distances:\n", + " ... # execute body\n", + " result_dict = {\"distance\": distance, \"total_energy\": result.eigenvalue + nuclear_repulsion_energy}\n", + " global_results.append(result_dict)\n", + " except Exception:\n", + " global_results.append({\"exception\": traceback.format_exc()})" + ], + "id": "fb84e1461cd3efcb", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Print the output to see the total energy for different distances:", + "id": "3be6069522c094ba" + }, + { + "metadata": {}, + "cell_type": "code", + "source": "print_output()", + "id": "378ee61864fa2526", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "For a visual representation, you can plot the total energy vs the distance:", + "id": "2d1e81329d1eb698" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from json import loads\n", + "import matplotlib.pyplot as plt\n", + "\n", + "def get_results():\n", + " output = get_output()\n", + " if Config.run_local:\n", + " results = loads(output[-1].replace(\"'\", '\"'))[\"results\"]\n", + " else:\n", + " results = loads(output.split(\"final_result=\")[-1].replace(\"'\", '\"'))[\"results\"]\n", + " return results\n", + "\n", + "def plot_output():\n", + " results = get_results()\n", + " distances, energies = [[result[key] for result in results] for key in [\"distance\", \"total_energy\"]]\n", + " fig, ax = plt.subplots()\n", + " ax.plot(distances, energies)\n", + " ax.set_xlabel(\"Distance (Angstrom)\")\n", + " ax.set_ylabel(\"Potential energy (a.u.)\")\n", + " return fig, ax\n", + "\n", + "plot_output()" + ], + "id": "2c9176f1873ad3c7", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "You can see a decreasing function in energy, the atoms start repelling each other at low distances, but the graph is noisy and there is no clear minimal distance.", + "id": "30c20be68b6169db" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Changing optimizer settings\n", + "\n", + "Let's try again with more iterations for the optimizer:" + ], + "id": "12532766a5255712" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# optimizer = COBYLA(maxiter=10) # replace in file\n", + "\n", + "plot_output()" + ], + "id": "3cd4588bbfe09334", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "And then some more iterations (this may take some minutes)", + "id": "18bf8f2a8edddf32" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# optimizer = COBYLA(maxiter=100) # replace in file\n", + "\n", + "plot_output()" + ], + "id": "5be01bb287638732", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Other optimizers are available in `qiskit_algorithms.optimizers` (https://qiskit-community.github.io/qiskit-algorithms/apidocs/qiskit_algorithms.optimizers.html). Finding the best one depends on the problem.", + "id": "1b1cc8d9d57d3a6e" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# from qiskit_algorithms.optimizers import NELDER_MEAD\n", + "# \n", + "# optimizer = NELDER_MEAD(maxiter=10) # replace in file\n", + "\n", + "plot_output()" + ], + "id": "7c4ec6665a1799d2", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Printing the circuit\n", + "\n", + "You can print the circuit that is executed. First we add the circuit to the results, in the file, next we print it with the `print_circuit` function. " + ], + "id": "caa4cf925d934c99" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from qiskit import transpile\n", + "\n", + "transpiled_circuit = transpile(result.optimal_circuit, basis_gates=['ry', 'h', 'cx', 'x', 'sdg', 'rz', 's'])\n", + "result_dict[\"circuit\"] = repr(transpiled_circuit.draw()).replace(\"'\", '\"')\n", + "result_dict[\"circuit_depth\"] = transpiled_circuit.depth()" + ], + "id": "7edac0283f5608f8", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "", + "id": "f6357e31c4ddbb2e" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from json import loads\n", + "\n", + "def print_circuit(index: int = 0):\n", + " results = get_results()\n", + " circuits, circuit_depths = [[result[key] for result in results] for key in [\"circuit\", \"circuit_depth\"]]\n", + " print(circuits[index])\n", + " print(f\"circuit_depth={circuit_depths[index]}\")\n", + " \n", + "print_circuit()" + ], + "id": "d605454a9ede6840", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Changing the qubit encoding\n", + "\n", + "We can use a different encoding, e.g. the Jordan-Wigner encoding, instead of the parity encoding." + ], + "id": "ee8f86ac5c47b80b" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# from qiskit_nature.second_q.mappers import JordanWignerMapper\n", + "# \n", + "# mapper = JordanWignerMapper() # replace in file\n", + "\n", + "print_circuit()" + ], + "id": "5e209f7bd2c9090a", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Running different optimization problems with VQE\n", + "\n", + "Instead of the hydrogen molecule, the VQE algorithm can also be used for different optimization problems. For this, we will replace the `qubit_op` (what we will measure), and the `ansatz` (how we will change the state to be measured). We will measure the operator `X + distance * Z` on one qubit. We have two parameters to optimize, which perform a rotation around the y-axis and then around the z-axis." + ], + "id": "71fece5c28338049" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# replace in the file\n", + "from qiskit.quantum_info import SparsePauliOp\n", + "\n", + "qubit_op = SparsePauliOp([\"X\", \"Z\"], coeffs=np.array([1, distance])) # e.g. 3-qubit bitstrings could be [\"XXY\", \"ZYX\"]\n", + "nuclear_repulsion_energy = 0\n", + "\n", + "from qiskit import QuantumCircuit\n", + "from qiskit.circuit import Parameter\n", + "\n", + "params = [Parameter(\"t_0\"), Parameter(\"t_1\")]\n", + "ansatz = QuantumCircuit(1) # 1 = number of qubits\n", + "ansatz.ry(params[0], qubit=0)\n", + "ansatz.rz(params[1], qubit=0)\n", + "\n", + "# add to the file\n", + "result_dict[\"optimal_t_0\"] = result.optimal_parameters[params[0]]\n", + "result_dict[\"optimal_t_1\"] = result.optimal_parameters[params[1]]" + ], + "id": "d94d3ecff8fb7e0d", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Next we will plot the optimal parameters and the calculated \"energy\"", + "id": "626ab8b8d9863237" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from json import loads\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "def plot_outputs():\n", + " results = get_results()\n", + " keys = [\"distance\", \"total_energy\", \"optimal_t_0\", \"optimal_t_1\"]\n", + " distances, energies, optimal_t_0s, optimal_t_1s = [[result[key] for result in results] for key in keys]\n", + " fig, ax = plt.subplots()\n", + " ax.plot(distances, energies, label=\"Total Energy (a.u.)\")\n", + " ax.plot(distances, np.mod(optimal_t_0s, 2*np.pi) - np.pi, label=\"t_0 (rad)\")\n", + " ax.plot(distances, np.mod(optimal_t_1s, 2*np.pi) - np.pi, label=\"t_1 (rad)\")\n", + " ax.set_xlabel(\"Distance (Angstrom)\")\n", + " ax.legend()\n", + " return fig, ax\n", + "\n", + "plot_outputs()" + ], + "id": "6587e1cab0c242a", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "Does it make sense what you see as optimization results?\n", + "\n", + "Can you make an optimization problem with two qubits? With three parameters? With more Pauli strings?\n", + "\n", + "What else can you think of?" + ], + "id": "6494604f5be12c96" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Running the algorithm locally on a Windows computer\n", + "\n", + "The pyscf package is only available on Linux, which the platform has. If you want to run the algorithm locally on Windows, the electronic structure information can be loaded from a pre-made file.\n", + "\n", + "Replace the calculation of `nuclear_repulsion_energy`, `n_particles`, `n_spatial_orbitals`, and `fermionic_op` with the following lines." + ], + "id": "99edb42031a2154e" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "from pathlib import Path\n", + "from json import loads\n", + "from qiskit_nature.second_q.operators import FermionicOp\n", + "\n", + "es_problem_data = loads((Path.cwd() / \"hydrogen_qiskit.json\").read_text())\n", + "idx = min(enumerate(es_problem_data), key=lambda x: abs(x[1][\"distance\"] - distance))[0]\n", + "val_at_distance = es_problem_data[idx]\n", + "\n", + "fermionic_op = FermionicOp(val_at_distance[\"fermionic_op\"])\n", + "n_particles = val_at_distance[\"n_particles\"]\n", + "n_spatial_orbitals = val_at_distance[\"n_spatial_orbitals\"]\n", + "nuclear_repulsion_energy = val_at_distance[\"nuclear_repulsion_energy\"]" + ], + "id": "ad03ad59e6da52e1", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "", + "id": "19d378513f52fd5d" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/hydrogen_qiskit.json b/examples/hydrogen_qiskit.json new file mode 100644 index 00000000..4545e2ae --- /dev/null +++ b/examples/hydrogen_qiskit.json @@ -0,0 +1,1130 @@ +[ + { + "distance": 0.735, + "nuclear_repulsion_energy": 0.7199689944489797, + "n_particles": [ + 1, + 1 + ], + "n_spatial_orbitals": 2, + "fermionic_op": { + "+_0 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dataclasses import dataclass -from typing import Any, Dict, List +from pathlib import Path +from typing import Any +import numpy as np +from qiskit import transpile from qiskit.primitives import BackendEstimator -from qiskit_algorithms import NumPyMinimumEigensolverResult, VQEResult +from qiskit_algorithms import VQEResult from qiskit_algorithms.minimum_eigensolvers import VQE from qiskit_algorithms.optimizers import COBYLA from qiskit_nature.second_q.circuit.library import UCCSD, HartreeFock from qiskit_nature.second_q.drivers import PySCFDriver from qiskit_nature.second_q.mappers import ParityMapper +from quantuminspire.sdk.models.hybrid_algorithm import HybridAlgorithm from quantuminspire.sdk.qiskit.backend import QuantumInspireBackend +from quantuminspire.util.api.local_backend import LocalBackend from quantuminspire.util.api.quantum_interface import QuantumInterface @dataclass class _GroundStateEnergyResults: - result: VQEResult | NumPyMinimumEigensolverResult + result: VQEResult nuclear_repulsion_energy: float def calculate_H0(backend: QuantumInspireBackend, distance: float = 0.735) -> _GroundStateEnergyResults: - - mapper = ParityMapper(num_particles=(1, 1)) molecule = f"H 0.0 0.0 0.0; H 0.0 0.0 {distance}" driver = PySCFDriver(molecule) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=UserWarning) - es_problem = driver.run() + es_problem = driver.run() fermionic_op = es_problem.hamiltonian.second_q_op() - qubit_op = mapper.map(fermionic_op) n_particles = es_problem.num_particles n_spatial_orbitals = es_problem.num_spatial_orbitals - nuclear_repulsion_energy = es_problem.nuclear_repulsion_energy - initial_state = HartreeFock(n_spatial_orbitals, n_particles, mapper) - ansatz = UCCSD(n_spatial_orbitals, n_particles, mapper, initial_state=initial_state) + mapper = ParityMapper(num_particles=(1, 1)) + qubit_op = mapper.map(fermionic_op) - optimizer = COBYLA(maxiter=1) # 10 iterations take two minutes estimator = BackendEstimator(backend=backend) + initial_state = HartreeFock(n_spatial_orbitals, n_particles, mapper) + ansatz = UCCSD(n_spatial_orbitals, n_particles, mapper, initial_state=initial_state) + optimizer = COBYLA(maxiter=1) # change to maxiter=100 for decent result algo = VQE(estimator, ansatz, optimizer) result = algo.compute_minimum_eigenvalue(qubit_op) - print(f"{distance=}: nuclear_repulsion_energy={nuclear_repulsion_energy}, eigenvalue={result.eigenvalue}") + print(f"{distance=}: nuclear_repulsion_energy={nuclear_repulsion_energy:.2f}, eigenvalue={result.eigenvalue:.2f}") return _GroundStateEnergyResults(result, nuclear_repulsion_energy) def execute(qi: QuantumInterface) -> None: - c = calculate_H0(backend=QuantumInspireBackend(qi)) - print(c) - - -def finalize(list_of_measurements: Dict[int, List[Any]]) -> Dict[str, Any]: - return {"measurements": list_of_measurements} + distances = np.arange(0.3, 0.5, 0.1) + results = [] + for distance in distances: + ground_state_energy_results = calculate_H0(backend=QuantumInspireBackend(qi), distance=distance) + result = {} + result["total_energy"] = ground_state_energy_results.nuclear_repulsion_energy + ground_state_energy_results.result.eigenvalue + result["distance"] = distance + results.append(result) + + qi.results = {"results": results} + + +def finalize(results: Any) -> dict[str, Any]: + return results + + +if __name__ == "__main__": + # Run the individual steps for debugging + algorithm = HybridAlgorithm("test", "test") + algorithm.read_file(Path(__file__)) + + # from quantuminspire.util.api.remote_backend import RemoteBackend + # backend = RemoteBackend() + # job_id = backend.run(algorithm, backend_type_id=3, number_of_shots=1024) + # results = backend.get_results(job_id) + + local_backend = LocalBackend() + job_id = local_backend.run(algorithm, 0) + results = local_backend.get_results(job_id)["results"] + print("=== Execute ===\n", results) + + # plot total energy vs distance between atoms + # import 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algorithm: HybridAlgorithm, quantum_interface: QuantumInter def run_quantum(self, circuit: str, number_of_shots: int = 1) -> ExecuteCircuitResult: """Execute provided circuit.""" result = self._qxelarator.execute_string(circuit, iterations=number_of_shots) + # todo: use result.results instead of result.state. qxelerator has a bug where it returns only "00" for + # result.results, but result.state contains the correct probability for all multi-qubit states. + results = {key: int(round(number_of_shots * abs(val) ** 2)) for key, val in result.state.items()} return ExecuteCircuitResult( - results=result.results, + results=results, shots_done=result.shots_done, shots_requested=result.shots_requested, )