Efficient molecular conformation generation with quantum-inspired algorithm
- URL: http://arxiv.org/abs/2404.14101v1
- Date: Mon, 22 Apr 2024 11:40:08 GMT
- Title: Efficient molecular conformation generation with quantum-inspired algorithm
- Authors: Yunting Li, Xiaopeng Cui, Zhaoping Xiong, Zuoheng Zou, Bowen Liu, Bi-Ying Wang, Runqiu Shu, Huangjun Zhu, Nan Qiao, Man-Hong Yung,
- Abstract summary: We propose the use of quantum-inspired algorithm to solve the molecular unfolding (MU) problem.
The root-mean-square deviation between the conformation determined by our approach and density functional theory (DFT) is negligible.
Results indicate that quantum-inspired algorithms can be applied to solve practical problems even before quantum hardware become mature.
- Score: 4.625636280559916
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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