jVMC: Versatile and performant variational Monte Carlo leveraging automated differentiation and GPU acceleration
arXiv:2108.03409 · doi:10.21468/SciPostPhysCodeb.2
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.
33 pages, 7 figures. Revised version. Code repository: https://github.com/markusschmitt/vmc_jax
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- Wave function network description and Kolmogorov complexity of quantum many-body systems
- Dynamics of correlation spreading in low-dimensional transverse-field Ising models
- Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks
- Zero-temperature Monte Carlo simulations of two-dimensional quantum spin glasses guided by neural network states
- Efficient Optimization of Variational Autoregressive Networks with Natural Gradient
- Simulating dynamics of the two-dimensional transverse-field Ising model: a comparative study of large-scale classical numerics