jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
Markus Schmitt, Moritz Reh
Abstract
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.
Topics & Concepts
Python (programming language)Computer scienceAnsatzAutomatic differentiationMonte Carlo methodComputational scienceCodebaseQuantum Monte CarloVariational Monte CarloImplementationAccelerationParallel computingAlgorithmSoftwarePhysicsMathematicsComputationProgramming languageQuantum mechanicsOperating systemClassical mechanicsStatisticsQuantum many-body systemsModel Reduction and Neural NetworksQuantum Computing Algorithms and Architecture