Simulation of implementable quantum-assisted genetic algorithm

Abstract

Quantum-assisted algorithms are expected to improve the computing performance of classical computers. A quantum genetic algorithm utilizes the advantages of quantum computation by combining the truncation selection in a classical genetic algorithm with the quantum Grover's algorithm. The parallelism of evaluation can create global search and reduce the need of crossover and mutation in a conventional genetic algorithm. In this work, we aim to demonstrate and simulate the performance of an implementable quantum-assisted genetic algorithm. The algorithm was tested by using quadratic unconstrained binary optimization (QUBO) for 100 iterations; and the results were compared with those from a classical counterpart for 2000 iterations, where both simulations were performed over 100 repetitions. The results showed that the quantum algorithm converges to the optimal solution faster. While the variance is higher at early stage, it quickly and greatly reduces as the algorithm converges. The histograms of possible solutions consistently exhibits this behavior.