Designing quantum many-body matter with conditional generative adversarial networks

TL;DR

This paper introduces a conditional GAN approach to efficiently simulate and analyze the dynamical properties of quantum many-body systems across their full parameter space, including disorder effects.

Contribution

It presents a novel application of conditional GANs for instant, accurate dynamical spectra prediction and Hamiltonian learning in complex quantum many-body systems.

Findings

Conditional GANs accurately reproduce dynamical spectra.

Method enables Hamiltonian learning from dynamical data.

Outlier detection flags non-physical systems.

Abstract

The computation of dynamical correlators of quantum many-body systems represents an open critical challenge in condensed matter physics. While powerful methodologies have risen in recent years, covering the full parameter space remains unfeasible for most many-body systems with a complex configuration space. Here we demonstrate that conditional Generative Adversarial Networks (GANs) allow simulating the full parameter space of several many-body systems, accounting both for controlled parameters, and stochastic disorder effects. After training with a restricted set of noisy many-body calculations, the conditional GAN algorithm provides the whole dynamical excitation spectra for a Hamiltonian instantly and with an accuracy analogous to the exact calculation. We further demonstrate how the trained conditional GAN automatically provides a powerful method for Hamiltonian learning from its dynamical excitations, and to flag non-physical systems via outlier detection. Our methodology puts forward generative adversarial learning as a powerful technique to explore complex many-body phenomena, providing a starting point to design large-scale quantum many-body matter.