Basis dependence of Neural Quantum States for the Transverse Field Ising Model

TL;DR

This paper explores how the effectiveness of Neural Quantum States depends on the choice of computational basis, linking basis-dependent properties to performance and providing insights for optimal basis selection.

Contribution

It introduces a framework connecting basis-dependent properties of ground states to NQS performance, focusing on the transverse-field Ising model and cluster expansion convergence.

Findings

Performance dependence is linked to ground state degeneracies and amplitude uniformity.

A cluster expansion framework explains basis dependence and convergence.

Insights help identify optimal bases for quantum state representations.

Abstract

Neural Quantum States (NQS) are powerful tools used to represent complex quantum many-body states in an increasingly wide range of applications. However, despite their popularity, at present only a rudimentary understanding of their limitations exists. In this work, we investigate the dependence of NQS on the choice of the computational basis, focusing on restricted Boltzmann machines. Considering a family of rotated Hamiltonians corresponding to the paradigmatic transverse-field Ising model, we discuss the properties of ground states responsible for the dependence of NQS performance, namely the presence of ground state degeneracies as well as the uniformity of amplitudes and phases, carefully examining their interplay. We identify that the basis-dependence of the performance is linked to the convergence properties of a cluster or cumulant expansion of multi-spin operators -- providing a framework to directly connect physical, basis-dependent properties, to performance itself. Our results provide insights that may be used to gauge the applicability of NQS to new problems and to identify the optimal basis for numerical computations.