Designing quantum many-body matter with conditional generative adversarial networks

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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 for 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 how to flag nonphysical 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.
Original languageEnglish
Article number033223
Pages (from-to)1-17
Number of pages17
Issue number3
Publication statusPublished - 19 Sep 2022
MoE publication typeA1 Journal article-refereed


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