Incorporating Quantum Advantage in Quantum Circuit Generation through Genetic Programming
This work addresses the challenge of automated quantum circuit design for researchers in quantum computing, offering incremental improvements by integrating quantum advantage into existing genetic algorithm frameworks.
The paper tackled the problem of designing efficient quantum circuits by proposing two novel approaches to incorporate quantum advantage metrics into the fitness function of genetic algorithms, resulting in improved convergence speed and circuits comparable to expert-designed solutions in test cases like the Bernstein-Vazirani and Unstructured Database Search Problems.
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.