Efficient molecular conformation generation with quantum-inspired algorithm
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916217401376768 |
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| author | Li, Yunting Cui, Xiaopeng Xiong, Zhaoping Zou, Zuoheng Liu, Bowen Wang, Bi-Ying Shu, Runqiu Zhu, Huangjun Qiao, Nan Yung, Man-Hong |
| author_facet | Li, Yunting Cui, Xiaopeng Xiong, Zhaoping Zou, Zuoheng Liu, Bowen Wang, Bi-Ying Shu, Runqiu Zhu, Huangjun Qiao, Nan Yung, Man-Hong |
| contents | Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_14101 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient molecular conformation generation with quantum-inspired algorithm Li, Yunting Cui, Xiaopeng Xiong, Zhaoping Zou, Zuoheng Liu, Bowen Wang, Bi-Ying Shu, Runqiu Zhu, Huangjun Qiao, Nan Yung, Man-Hong Quantum Physics Chemical Physics Conformation generation, also known as molecular unfolding (MU), is a crucial step in structure-based drug design, remaining a challenging combinatorial optimization problem. Quantum annealing (QA) has shown great potential for solving certain combinatorial optimization problems over traditional classical methods such as simulated annealing (SA). However, a recent study showed that a 2000-qubit QA hardware was still unable to outperform SA for the MU problem. Here, we propose the use of quantum-inspired algorithm to solve the MU problem, in order to go beyond traditional SA. We introduce a highly-compact phase encoding method which can exponentially reduce the representation space, compared with the previous one-hot encoding method. For benchmarking, we tested this new approach on the public QM9 dataset generated by density functional theory (DFT). The root-mean-square deviation between the conformation determined by our approach and DFT is negligible (less than about 0.5 Angstrom), which underpins the validity of our approach. Furthermore, the median time-to-target metric can be reduced by a factor of five compared to SA. Additionally, we demonstrate a simulation experiment by MindQuantum using quantum approximate optimization algorithm (QAOA) to reach optimal results. These results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature. |
| title | Efficient molecular conformation generation with quantum-inspired algorithm |
| topic | Quantum Physics Chemical Physics |
| url | https://arxiv.org/abs/2404.14101 |