jVMC：利用自动微分和GPU加速的多功能和高性能变分蒙特卡罗
jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
摘要：
神经量子态（NQS）的引入给变分蒙特卡罗（VMC）带来了新的发展。系统地减少波函数分析器的偏差的能力使得该方法具有广泛的适用性。然而，高性能的实现对于达到最先进的数值水平至关重要。在这里，我们提出了一个支持任意NQS架构和模型Hamilton的Python代码库。此外，它还利用自动区分、加速器的即时编译和分布式计算，旨在促进高效NQS算法的组合。
英文摘要：
The introduction of Neural Quantum States (NQS) has recently given a new twist to variational Monte Carlo (VMC). The ability to systematically reduce the bias of the wave function ansatz renders the approach widely applicable. However, performant implementations are crucial to reach the numerical state of the art. Here, we present a Python codebase that supports arbitrary NQS architectures and model Hamiltonians. Additionally leveraging automatic differentiation, just-in-time compilation to accelerators, and distributed computing, it is designed to facilitate the composition of efficient NQS algorithms.
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