  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.
