Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866914010434109440 |
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| author | Jahin, Afrar Pan, Yi Wang, Yingfeng Liu, Tianming Zhang, Wei |
| author_facet | Jahin, Afrar Pan, Yi Wang, Yingfeng Liu, Tianming Zhang, Wei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19394 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding Jahin, Afrar Pan, Yi Wang, Yingfeng Liu, Tianming Zhang, Wei Machine Learning Quantum Physics 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. |
| title | Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding |
| topic | Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2508.19394 |