This repo contains the code for executing Hybrid Quantum Genetic Algorithm (HQGA) proposed in:
''G. Acampora and A. Vitiello, "Implementing evolutionary optimization on actual quantum processors," in Information Sciences, 2021, doi: 10.1016/j.ins.2021.06.049.''
The package can be installed with Python's pip package manager.
pip install HQGAThis is a basic example to use HQGA for solving Sphere problem on Qasm Simulator.
from HQGA import problems as p, hqga_utils, utils, hqga_algorithm
from HQGA.utils import computeHammingDistance
from qiskit import Aer
import math
simulator = Aer.get_backend('qasm_simulator')
device_features= hqga_utils.device(simulator, False)
params= hqga_utils.ReinforcementParameters(3, 5, math.pi / 16, math.pi / 16, 0.3)
params.draw_circuit=True
problem = p.SphereProblem(num_bit_code=5)
circuit = hqga_utils.setupCircuit(params.pop_size, problem.dim * problem.num_bit_code)
gBest, chromosome_evolution,bests = hqga_algorithm.runQGA(device_features, circuit, params,problem)
dist=computeHammingDistance(gBest.chr, problem)
print("The Hamming distance to the optimum value is: ", dist)
utils.writeBestsXls("Bests.xlsx", bests)
utils.writeChromosomeEvolutionXls("ChromosomeEvolution.xlsx", chromosome_evolution)Please cite the work using the following Bibtex entry:
@article{ACAMPORA2021542,
title = {Implementing evolutionary optimization on actual quantum processors},
journal = {Information Sciences},
volume = {575},
pages = {542-562},
year = {2021},
issn = {0020-0255},
doi = {https://doi.org/10.1016/j.ins.2021.06.049},
url = {https://www.sciencedirect.com/science/article/pii/S002002552100640X},
author = {Giovanni Acampora and Autilia Vitiello}
}