Comparison of performance of amazon braket using a quantum genetic algorithm

Abstract

This study evaluates the performance of a Quantum Genetic Algorithm (QGA) executed on quantum devices. The algorithm was implemented in MATLAB and Python and deployed on the Amazon Web Services (AWS) platform. The QGA was utilized to optimize a set of continuous single-variable functions. The implementation employed the Hadamard quantum gate to initialize the population and the Ry rotation gate for mutation and crossover. The findings revealed significant differences in execution time and costs were observed between the two implementations, undescoring the performance of quantum devices available on AWS. The results demonstrate that the QGA can achieve optimal solutions in a few generations, suggesting its potential for efficiently solving complex problems. However, the costs and availability of quantum devices remain restrictive. This work exemplifies the potential of leveraging AWS cloud-based quantum computing platforms for the research and development of quantum algorithms.