Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding

• URL: http://arxiv.org/abs/2508.19394v2
• Date: Thu, 28 Aug 2025 03:34:01 GMT
• Title: Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
• Authors: Afrar Jahin, Yi Pan, Yingfeng Wang, Tianming Liu, Wei Zhang,
• Abstract summary: We propose a hybrid quantum-classical architecture for SMILES reconstruction that integrates quantum encoding with classical sequence modeling.<n>Our approach achieves a quantum fidelity of approximately 84% and a classical reconstruction similarity of 60%, surpassing existing quantum baselines.
• Score: 11.627919867400905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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