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This research introduces a specialized neural network architecture designed to numerically model complex many-body systems where atoms interact via both direct contact and long-range photon-mediated forces. By successfully handling hybrid Hilbert spaces in strongly coupled spin-photon lattices, the method reveals significant deviations from traditional mean-field theories regarding ground state phase boundaries. This scalable framework represents a notable advancement at the intersection of quantum simulation techniques and machine learning for studying light-matter interactions.
Modeling light-matter coupled systems with neural quantum states
arXiv:2606.14352v1 Announce Type: cross Abstract: Recent advances in cold atom manipulation enable the study of many-body systems where short-range interactions between neighboring atoms coexist with long-range interactions mediated by photons. Such a combination of interactions makes a theoretical approach challenging beyond mean-field methods. In this work, we develop a neural quantum state based approach to study these systems numerically. We introduce a neural-network architecture capable of handling hybrid Hilbert spaces with large local bosonic dimensions in strongly interacting spin-photon systems. We benchmark this approach on a model of a two-dimensional lattice of Rydberg atoms coupled to a photon mode. The superradiant ground states found in the large spin-photon coupling regime allow us to demonstrate the efficiency of the method in the presence of high photon occupation. Furthermore, the ability to capture spin-spin and spin-photon correlations leads us to observe quantitative deviations in the ground state phase boundaries with respect to mean-field theory. The method extends to other systems with a similar hybrid Hilbert space structure, such as spin-phonon systems, and provides a scalable framework for investigating their ground state properties.
Abstract:Recent advances in cold atom manipulation enable the study of many-body systems where short-range interactions between neighboring atoms coexist with long-range interactions mediated by photons. Such a combination of interactions makes a theoretical approach challenging beyond mean-field methods. In this work, we develop a neural quantum state based approach to study these systems numerically. We introduce a neural-network architecture capable of handling hybrid Hilbert spaces with large local bosonic dimensions in strongly interacting spin-photon systems. We benchmark this approach on a model of a two-dimensional lattice of Rydberg atoms coupled to a photon mode. The superradiant ground states found in the large spin-photon coupling regime allow us to demonstrate the efficiency of the method in the presence of high photon occupation. Furthermore, the ability to capture spin-spin and spin-photon correlations leads us to observe quantitative deviations in the ground state phase boundaries with respect to mean-field theory. The method extends to other systems with a similar hybrid Hilbert space structure, such as spin-phonon systems, and provides a scalable framework for investigating their ground state properties.
15 pages, 8 figures, The data associated with this manuscript version is available under DOI:https://doi.org/10.5281/zenodo.20630702
Quantum Gases (cond-mat.quant-gas); Quantum Physics (quant-ph)
arXiv:2606.14352 [cond-mat.quant-gas]
(or arXiv:2606.14352v1 [cond-mat.quant-gas] for this version)
https://doi.org/10.48550/arXiv.2606.14352 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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