对比多种突变策略，提升量子电路合成效率
Evaluating Mutation Techniques in Genetic Algorithm-Based Quantum Circuit Synthesis
- 结合删除与交换突变策略优化量子电路
- 在4至6量子比特电路中实现更高保真度与更低深度
- 适合量子算法开发者与优化框架研究者
量子计算利用量子比特特性和量子并行性解决经典系统无法处理的问题，具有巨大计算潜力。然而，量子电路优化对噪声中等规模量子（NISQ）设备而言至关重要，因其量子比特有限且错误率高。遗传算法（GAs）通过自动化优化任务，为高效量子电路合成提供了可行路径。本文研究了在GA框架下不同突变策略对量子电路合成的影响。通过分析各类突变对电路的转化效果，识别出能提升效率与性能的策略。实验采用以保真度为核心、兼顾电路深度与T门数量的适应度函数，针对4至6量子比特电路进行优化。全面超参数测试表明，结合删除与交换策略优于其他方法，验证了其在构建鲁棒的基于GA的量子电路优化器中的有效性。
原文摘要 · Abstract (English)
Quantum computing leverages the unique properties of qubits and quantum parallelism to solve problems intractable for classical systems, offering unparalleled computational potential. However, the optimization of quantum circuits remains critical, especially for noisy intermediate-scale quantum (NISQ) devices with limited qubits and high error rates. Genetic algorithms (GAs) provide a promising approach for efficient quantum circuit synthesis by automating optimization tasks. This work examines the impact of various mutation strategies within a GA framework for quantum circuit synthesis. By analyzing how different mutations transform circuits, it identifies strategies that enhance efficiency and performance. Experiments utilized a fitness function emphasizing fidelity, while accounting for circuit depth and T operations, to optimize circuits with four to six qubits. Comprehensive hyperparameter testing revealed that combining delete and swap strategies outperformed other approaches, demonstrating their effectiveness in developing robust GA-based quantum circuit optimizers.
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