基于量子电路演化的分子对接
Molecular Docking with Quantum Circuit Evolution
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中文总结 AI 辅助
研究旨在解决分子对接高计算成本问题，提出基于量子电路演化（QCE）的方法，该方法是基于门且无梯度的量子进化方法，能以更少步骤找到最佳解决方案，实现快速稳定收敛。
中文摘要 AI 辅助
分子对接是药物发现中的重要步骤，可评估受体-配体亲和力，降低实验成本并增加测试数量。然而，其高计算成本限制了实验精度和问题规模。近期有工作提出用基于高斯玻色子采样量子计算机和门控量子计算机的量子算法。本文提出用量子电路演化（QCE）解决分子对接问题，这是一种基于门且无梯度的量子进化方法，通过对量子电路随机应用酉运算驱动进化。该算法能比先前研究方法用更少步骤找到问题的最佳解决方案，收敛快速且稳定。
英文摘要
Molecular docking is an important step in drug discovery, enabling the evaluation of receptor-ligand affinity while reducing experimental costs and increasing the number of possible tests. However, the high computational cost associated with molecular docking remains a limiting factor that can restrict both the experimental precision and the scale of the problems being addressed. To improve the future applicability of molecular docking, recent works have proposed the use of quantum algorithms based on Gaussian Boson Sampling quantum computers and also gate-based quantum computers. In this work, we propose the use of Quantum Circuit Evolution (QCE) for solving the molecular docking problem, a gate based and gradient-free quantum evolutionary method whose evolution is driven by the random application of unitary operations to a quantum circuit. The proposed algorithm demonstrated the ability to find the best solution to the problem in fewer steps than the methods presented in previous studies, exhibiting fast and stable convergence.
