Quantum-Classical Hybrid Molecular Autoencoder for Advancing Classical Decoding

TL;DR

This paper introduces a hybrid quantum-classical model for SMILES string reconstruction that improves quantum fidelity and classical similarity, advancing molecular generative modeling.

Contribution

It presents a novel hybrid architecture combining quantum encoding with classical sequence modeling for improved molecular string reconstruction.

Findings

Quantum fidelity of approximately 84% achieved.

Classical reconstruction similarity of 60% surpassed existing baselines.

Lays foundation for quantum-aware sequence models in molecular discovery.

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.