MolQAE: Quantum Autoencoder for Molecular Representation Learning
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916766824792064 |
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| author | Pan, Yi Jiang, Hanqi Ruan, Wei Zhu, Dajiang Li, Xiang Abate, Yohannes Wang, Yingfeng Liu, Tianming |
| author_facet | Pan, Yi Jiang, Hanqi Ruan, Wei Zhu, Dajiang Li, Xiang Abate, Yohannes Wang, Yingfeng Liu, Tianming |
| contents | We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecular characteristics and a remarkable preservation of fidelity, approaching robust molecular reconstruction even with substantial dimensionality reduction. Our model establishes a quantum pathway in cheminformatics by being the first to process complete molecular structural information with a dedicated quantum architecture considering the Noisy Intermediate-Scale Quantum (NISQ)-era development and promising significant advances in drug and materials discovery. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MolQAE: Quantum Autoencoder for Molecular Representation Learning Pan, Yi Jiang, Hanqi Ruan, Wei Zhu, Dajiang Li, Xiang Abate, Yohannes Wang, Yingfeng Liu, Tianming Quantum Physics We introduce Quantum Molecular Autoencoder (MolQAE), the first quantum autoencoder to leverage the complete molecular structures. MolQAE uniquely maps SMILES strings directly to quantum states using parameterized rotation gates, preserving vital structural information. Its quantum encoder-decoder framework enables latent space compression and reconstruction. A dual-objective strategy optimizes fidelity and minimizes trash state deviation. Our evaluations demonstrate effective capture of molecular characteristics and a remarkable preservation of fidelity, approaching robust molecular reconstruction even with substantial dimensionality reduction. Our model establishes a quantum pathway in cheminformatics by being the first to process complete molecular structural information with a dedicated quantum architecture considering the Noisy Intermediate-Scale Quantum (NISQ)-era development and promising significant advances in drug and materials discovery. |
| title | MolQAE: Quantum Autoencoder for Molecular Representation Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2505.01875 |