Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming

TL;DR

This paper introduces two novel methods for integrating quantum advantage metrics into genetic algorithms to improve quantum circuit design efficiency, demonstrating faster convergence and high-quality circuits for key quantum problems.

Contribution

It proposes new approaches to incorporate quantum advantage into genetic algorithm fitness functions, enhancing automated quantum circuit generation.

Findings

Improved convergence speed of genetic algorithms.

Generated circuits comparable to expert designs.

Effective for Bernstein-Vazirani and database search problems.

Abstract

Designing efficient quantum circuits that leverage quantum advantage compared to classical computing has become increasingly critical. Genetic algorithms have shown potential in generating such circuits through artificial evolution. However, integrating quantum advantage into the fitness function of these algorithms remains unexplored. In this paper, we aim to enhance the efficiency of quantum circuit design by proposing two novel approaches for incorporating quantum advantage metrics into the fitness function of genetic algorithms.1 We evaluate our approaches based on the Bernstein-Vazirani Problem and the Unstructured Database Search Problem as test cases. The results demonstrate that our approaches not only improve the convergence speed of the genetic algorithm but also produce circuits comparable to expert-designed solutions. Our findings suggest that automated quantum circuit design using genetic algorithms that incorporate a measure of quantum advantage is a promising approach to accelerating the development of quantum algorithms.