用于量子分子生成的秩细化量子行为粒子群优化算法
Rank-Refined Quantum-Behaved Particle Swarm Optimization for Quantum Molecular Generation
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中文总结 AI 辅助
研究针对量子分子生成中的高维参数搜索问题，提出秩细化量子行为粒子群优化算法，结合多种策略改进优化效果，实验表明该算法能在不改变相关管道的情况下提升量子分子生成性能。
中文摘要 AI 辅助
本文提出了用于量子分子生成（QMG）中高维参数搜索的秩细化量子行为粒子群优化算法（RR-QPSO）。RR-QPSO针对由昂贵的目标评估导致的优化瓶颈，每个候选参数向量都需要随机电路采样、位串解码和分子评估。该方法为贝叶斯优化（BO）提供了基于种群的替代方案，结合了基于索博尔序列的初始化、秩细化的均值-最优更新以及基于有效性和唯一性的适应度引导细化。实验使用具有134个参数、20量子比特的CUDA-Q电路的9重原子QMG基准，并在8个NVIDIA V100 GPU上并行进行粒子评估。结果表明，RR-QPSO在不修改化学启发电路或分子解码管道的情况下，可通过优化器级设计改进QMG。
英文摘要
This work proposes Rank-Refined Quantum-Behaved Particle Swarm Optimization (RR-QPSO) for high-dimensional parameter search in Quantum Molecular Generation (QMG). RR-QPSO targets the optimization bottleneck caused by expensive objective evaluations, where each candidate parameter vector requires stochastic circuit sampling, bitstring decoding, and molecular evaluation. The method provides a population-based alternative to Bayesian optimization (BO), combining Sobol-based initialization, a rank-refined mean-best update, and fitness-guided refinement based on validity and uniqueness. Experiments use the 9-heavy-atom QMG benchmark with a 134-parameter, 20-qubit CUDA-Q circuit and particle evaluations parallelized across 8 NVIDIA V100 GPUs. With M=64 particles and T=150 iterations, RR-QPSO reaches VxU = 0.930; increasing the swarm size to M=128 further improves the product to 0.942, compared with 0.902 for BO under the same protocol. A multi-objective extension targeting HBA=4 and HBD=3 further shows that RR-QPSO can guide molecular properties while preserving a higher validity--uniqueness product than BO. These results suggest that optimizer-level design can improve QMG without modifying the chemistry-inspired circuit or molecular decoding pipeline.
