Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming

TL;DR

The paper tackles the challenge of automating quantum circuit design by ensuring generated circuits demonstrably exploit quantum advantage. It develops two fitness-function approaches—Direct and Indirect Quantum Advantage—to steer genetic algorithms toward circuits that not only solve target tasks but do so with a genuine speedup over classical methods. Empirical evaluation on Bernstein-Vazirani and Unstructured Database Search shows faster convergence and circuits that are often comparable to, or even innovative relative to, state-of-the-art designs, including diffusion-like operators reminiscent of Grover’s algorithm. This work suggests a promising route for accelerating automated quantum algorithm development by explicitly prioritizing quantum advantage during evolution, with potential impact on hardware-aware circuit synthesis and algorithm discovery.

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.

Figures (6)

• Figure 1: Averaged fitness values on the Bernstein-Vazirani Problem.
• Figure 2: Best performing circuits of each fitness function for the Bernstein-Vazirani Problem.
• Figure 3: Averaged fitness values on the Unstructured Database Search Problem, grouped by fitness function.
• Figure 4: Best performing circuit for BaselineFitness on the Unstructured Database Search Problem.
• Figure 5: Best performing circuit for DirectQAFitness on the Unstructured Database Search Problem.