用粒子群优化生成量子电路，比遗传算法更快找到最优解。
Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
- 将量子电路编码为粒子群参数，通过优化搜索电路结构。
- 实验显示粒子群算法收敛速度优于遗传算法。
- 适合对量子电路优化效率有要求的研究者参考。
本文探讨了粒子群优化（PSO）在求解MaxOne问题实例中生成量子电路的应用。通过简要介绍PSO的参数与算法流程，重点研究了将量子电路编码为PSO参数的方法。采用MaxOne问题作为适应度评估标准，对比分析了不同学习能力与惯性权重变化下的PSO性能。进一步将PSO与遗传算法在量子电路合成中的表现进行比较，结果表明PSO能更快速收敛至最优解。
原文摘要 · Abstract (English)
This paper discusses how particle swarm optimization (PSO) can be used to generate quantum circuits to solve an instance of the MaxOne problem. It then analyzes previous studies on evolutionary algorithms for circuit synthesis. With a brief introduction to PSO, including its parameters and algorithm flow, the paper focuses on a method of quantum circuit encoding and representation as PSO parameters. The fitness evaluation used in this paper is the MaxOne problem. The paper presents experimental results that compare different learning abilities and inertia weight variations in the PSO algorithm. A comparison is further made between the PSO algorithm and a genetic algorithm for quantum circuit synthesis. The results suggest PSO converges more quickly to the optimal solution.
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