MolQAE: Quantum Autoencoder for Molecular Representation Learning

• URL: http://arxiv.org/abs/2505.01875v1
• Date: Sat, 03 May 2025 17:36:47 GMT
• Title: MolQAE: Quantum Autoencoder for Molecular Representation Learning
• Authors: Yi Pan, Hanqi Jiang, Wei Ruan, Dajiang Zhu, Xiang Li, Yohannes Abate, Yingfeng Wang, Tianming Liu,
• Abstract summary: This paper introduces the Quantum Molecular Autoencoder, a novel approach that integrates quantum computing with molecular representation learning.<n>We present a quantum circuit-based autoencoder architecture that maps SMILES molecular representations into quantum state space.<n> Experimental results demonstrate that quantum autoencoders effectively capture molecular structures and chemical properties.
• Score: 19.646000097585272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces the Quantum Molecular Autoencoder, a novel approach that integrates quantum computing with molecular representation learning. While conventional molecular representation methods face computational bottlenecks when processing high-dimensional data, quantum computing offers a promising alternative through its inherent parallelism and quantum superposition properties. We present a quantum circuit-based autoencoder architecture that maps SMILES molecular representations into quantum state space, employs parameterized quantum circuits for dimensional reduction, and utilizes SWAP tests to evaluate encoding quality. Theoretically, our approach preserves essential molecular features in exponentially smaller spaces while maintaining similarity relationships between molecules. Experimental results demonstrate that quantum autoencoders effectively capture molecular structures and chemical properties. The proposed framework not only establishes a quantum pathway for molecular representation learning but also opens new possibilities for applications in drug discovery and materials design. As the first investigation at the intersection of molecular representation learning and quantum computing, this research lays both theoretical and practical foundations for the advancement of cheminformatics.

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