量子-经典混合架构提升分子字符串重建精度
Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
- 用量子编码结合经典序列模型重构SMILES字符串
- 量子保真度达84%，经典相似度达60%优于现有方法
- 适合关注量子机器学习与药物设计交叉的研究者
尽管量子机器学习（QML）在生成模型方面具有巨大潜力，尤其在分子设计领域，但大量经典方法仍难以实现高保真度和有效性。特别是将QML应用于基于序列的任务（如简化分子输入线性输入系统，SMILES字符串重建）的研究仍不充分，且常伴随保真度下降问题。本文提出一种量子-经典混合架构用于SMILES字符串重建，通过量子编码与经典序列建模的融合，提升量子保真度与经典相似度。实验结果显示，该方法达到约84%的量子保真度和60%的古典重建相似度，优于现有量子基线。本工作为未来QML应用奠定基础，平衡了量子表达能力与经典序列模型性能，推动了面向分子与药物发现的量子感知序列模型研究。
原文摘要 · Abstract (English)
Although recent advances in quantum machine learning (QML) offer significant potential for enhancing generative models, particularly in molecular design, a large array of classical approaches still face challenges in achieving high fidelity and validity. In particular, the integration of QML with sequence-based tasks, such as Simplified Molecular Input Line Entry System (SMILES) string reconstruction, remains underexplored and usually suffers from fidelity degradation. In this work, we propose a hybrid quantum-classical architecture for SMILES reconstruction that integrates quantum encoding with classical sequence modeling to improve quantum fidelity and classical similarity. Our approach achieves a quantum fidelity of approximately 84% and a classical reconstruction similarity of 60%, surpassing existing quantum baselines. Our work lays a promising foundation for future QML applications, striking a balance between expressive quantum representations and classical sequence models and catalyzing broader research on quantum-aware sequence models for molecular and drug discovery.
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