Variational learning of quantum ground states on spiking neuromorphic hardware

TL;DR

This paper demonstrates that neuromorphic hardware can effectively represent quantum ground states through variational energy minimization, offering a promising approach to overcome computational challenges in quantum many-body problems.

Contribution

It introduces a novel training algorithm for neuromorphic chips to model quantum ground states, showing successful application to the transverse field Ising model for small systems.

Findings

Neuromorphic hardware can represent quantum ground states effectively.

Performance depends on sample quality and hardware parameter stability.

Scalability is limited by temporal variations in the neuromorphic chip.

Abstract

Recent research has demonstrated the usefulness of neural networks as variational ansatz functions for quantum many-body states. However, high-dimensional sampling spaces and transient autocorrelations confront these approaches with a challenging computational bottleneck. Compared to conventional neural networks, physical-model devices offer a fast, efficient and inherently parallel substrate capable of related forms of Markov chain Monte Carlo sampling. Here, we demonstrate the ability of a neuromorphic chip to represent the ground states of quantum spin models by variational energy minimization. We develop a training algorithm and apply it to the transverse field Ising model, showing good performance at moderate system sizes ($N\leq 10$). A systematic hyperparameter study shows that scalability to larger system sizes mainly depends on sample quality, which is limited by temporal parameter variations on the analog neuromorphic chip. Our work thus provides an important step towards harnessing the capabilities of neuromorphic hardware for tackling the curse of dimensionality in quantum many-body problems.