用量子计算机采样稀有构象转变
Sampling Rare Conformational Transitions with a Quantum Computer
AI总结：
本文提出结合机器学习与量子计算的MD模拟新方法，用于高效采样稀有构象转变，通过量子退火机探索过渡路径集，验证了在原子级描述中模拟基准构象转变的可行性。
摘要：
自发的结构重排在复杂生物分子系统中的组织和功能中起核心作用。原则上，基于物理的计算机模拟如分子动力学（MD）使我们能够以原子级分辨率研究这些热激活过程。然而，稀有构象转变本质上难以用MD研究，因为必须投入指数级大的计算资源来模拟亚稳态中的热波动。路径采样方法如过渡路径采样有潜力将计算资源集中在采样亚稳态之间的稀有随机转变上。在这些方法中，一个突出的限制是生成访问构象空间显著不同区域的路径，而计算成本低。为克服这些问题，我们引入了一种严格的方法，将机器学习算法和在经典计算机上实现的MD模拟与绝热量子计算结合。首先，利用功能积分方法，我们推导出基于少量由机器学习生成的分子构型的系统动力学的低分辨率表示。然后，使用量子退火机探索该低分辨率理论的过渡路径集，而不引入非物理偏置力来引导系统的动力学。使用D-Wave量子计算机，我们通过模拟一个最先进的原子级描述中的基准构象转变验证了我们的方案。我们证明量子计算步骤生成不相关的轨迹，从而促进配置空间中过渡区域的采样。我们的结果为MD模拟提供了一种新的范式，将机器学习和量子计算整合在一起。
英文摘要：
Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these thermally activated processes with an atomic level of resolution. However, rare conformational transitions are intrinsically hard to investigate with MD, because an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods like Transition Path Sampling hold the great promise of focusing the available computational power on sampling the rare stochastic transition between metastable states. In these approaches, one of the outstanding limitations is to generate paths that visit significantly different regions of the conformational space at a low computational cost. To overcome these problems we introduce a rigorous approach that integrates a machine learning algorithm and MD simulations implemented on a classical computer with adiabatic quantum computing. First, using functional integral methods, we derive a rigorous low-resolution representation of the system's dynamics, based on a small set of molecular configurations generated with machine learning. Then, a quantum annealing machine is employed to explore the transition path ensemble of this low-resolution theory, without introducing un-physical biasing forces to steer the system's dynamics. Using the D-Wave quantum computer, we validate our scheme by simulating a benchmark conformational transition in a state-of-the-art atomistic description. We show that the quantum computing step generates uncorrelated trajectories, thus facilitating the sampling of the transition region in configuration space. Our results provide a new paradigm for MD simulations to integrate machine learning and quantum computing.
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