Genetic Algorithm Based Optimisation Framework for Quantum Circuit Mapping GAQCM

Abstract

Most of the existing qubit mapping algorithms are deterministic, resulting in a lack of diversity in the generated quantum circuit mappings, which makes it difficult to strike a balance between quality and diversity, and prevents them from being flexibly adapted to different quantum computing tasks. To solve this problem, this paper proposes the Genetic Algorithm-based Quantum Circuit Mapping (GAQCM) framework, which is based on the idea of genetic algorithm, evaluates the quality of mapping by the fitness function, and continuously improves the mapping scheme through several iterations. The GAQCM framework is designed to be flexible, providing a variety of choices, crossover operations and several unique mutation methods are designed to improve the performance of the algorithm. The framework also introduces a neighbourhood gate-based initialisation strategy to improve the algorithm efficiency and convergence speed. Experimental results show that the GAQCM framework reduces the number of SWAP gates by an average of 44.6% and 62.0%, and reduces the hardware gate overhead by 13.8% and 14.9% in the t|ket > and Qiskit compilers, respectively, compared to 2QAN. The framework allows users to customise the fitness function and parameter settings according to their specific needs, and is suitable for a variety of quantum computing tasks.