电子学报2024，Vol.52Issue(4)：1330-1336,7.DOI:10.12263/DZXB.20230832
一种模拟绝热量子计算的适应度地形探索算法
A Fitness Landscape Exploration Algorithm Simulating Adiabatic Quantum Computation
摘要
Abstract
After transforming an optimization problem into an objective function,the degree of matching between the objective function and the chosen heuristic optimization algorithm determines the efficiency of the following optimization.By simulating multi-ground states evolution in adiabatic quantum computation,a fitness landscape exploration algorithm is proposed to reflect the optimization characteristics of the objective function and guide the selection of optimization algo-rithms and their parameters.In quantum ground state evolution,the ground state wave function of a particle tends to con-verge towards regions with lower potential energy,and the extent of convergence is influenced by the quantum effect strength.Using these features,we encode the potential energy field by the objective function in a multi-ground states evolu-tion with diminishing quantum effect,and consequently the fitness landscape of the objective function is reflected by the dis-tributions of a set of converging ground state wave function in this adiabatic evolution.Based on the quantum path integral,the algorithm is implemented using a downscaling diffusion Monte Carlo(DMC).Experiments illustrated that the algorithm comprehensively and intuitively reflected numerous features of the fitness landscape,and the obtained information could di-rectly guide optimization thereafter.Its computational mode resembles that of heuristic optimization,as it does not introduce other computations during optimization.These features introduce a novel perspective to the study of fitness landscape.关键词
适应度地形/启发式优化/绝热量子计算/浸渐量子计算/基态演化/扩散蒙特卡罗/量子退火Key words
fitness landscape/heuristic optimization/adiabatic quantum computation/ground state evolution/diffu-sion Monte Carlo/quantum annealing分类
信息技术与安全科学引用本文复制引用
杨国松,王鹏,尹鑫钰..一种模拟绝热量子计算的适应度地形探索算法[J].电子学报,2024,52(4):1330-1336,7.基金项目
国家自然科学基金(No.60702075) （No.60702075）
四川省科技创新苗子工程项目(No.2019001) National Natural Science Foundation of China(No.60702075) （No.2019001）
Sichuan Science and Technol-ogy Program(No.2019001) （No.2019001）
