用优化架构提升量子-经典分子生成模型性能，加速药物研发。
Bridging Quantum and Classical Computing in Drug Design: Architecture Principles for Improved Molecule Generation
- 设计分层浅层量子电路串联的混合生成对抗网络
- 药物候选得分提升2.27倍，参数量减少60%以上
- 适合想落地量子计算的药企与研究团队参考
混合量子-经典机器学习为利用噪声中等规模量子（NISQ）设备进行药物发现提供了新路径，但最优模型架构尚不明确。我们通过多目标贝叶斯优化系统性地优化了生成对抗网络（GAN）的量子-经典桥接架构。所提出的优化模型（BO-QGAN）显著提升性能：药物候选得分（DCS）比以往量子混合基准高出2.27倍，比经典基线高2.21倍，同时参数量减少超过60%。关键发现指出，应采用3至4个浅层（4至8量子比特）量子电路依次串联的结构，而经典部分在达到最低容量后敏感度较低。本工作首次提供基于实证的混合模型架构指南，推动当前量子计算机更有效地融入制药研发流程。
原文摘要 · Abstract (English)
Hybrid quantum-classical machine learning offers a path to leverage noisy intermediate-scale quantum (NISQ) devices for drug discovery, but optimal model architectures remain unclear. We systematically optimize the quantum-classical bridge architecture of generative adversarial networks (GANs) for molecule discovery using multi-objective Bayesian optimization. Our optimized model (BO-QGAN) significantly improves performance, achieving a 2.27-fold higher Drug Candidate Score (DCS) than prior quantum-hybrid benchmarks and 2.21-fold higher than the classical baseline, while reducing parameter count by more than 60%. Key findings favor layering multiple (3-4) shallow (4-8 qubit) quantum circuits sequentially, while classical architecture shows less sensitivity above a minimum capacity. This work provides the first empirically-grounded architectural guidelines for hybrid models, enabling more effective integration of current quantum computers into pharmaceutical research pipelines.
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