Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
This work addresses fidelity degradation in quantum-classical integration for molecular design, offering incremental improvements for researchers in quantum machine learning and drug discovery.
The authors tackled the problem of low fidelity in quantum machine learning for SMILES string reconstruction by proposing a hybrid quantum-classical architecture, achieving approximately 84% quantum fidelity and 60% classical similarity, which surpasses existing quantum baselines.
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.