Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913926041567232 |
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| author | Hidayat, Mirza Hizriyan Nubli Cheah, Tan Chye |
| author_facet | Hidayat, Mirza Hizriyan Nubli Cheah, Tan Chye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights Hidayat, Mirza Hizriyan Nubli Cheah, Tan Chye Neural and Evolutionary Computing Artificial Intelligence 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. |
| title | Particle Swarm Optimization for Quantum Circuit Synthesis: Performance Analysis and Insights |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2507.02898 |