Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack

• URL: http://arxiv.org/abs/2507.13322v2
• Date: Sun, 03 Aug 2025 15:00:28 GMT
• Title: Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack
• Authors: Khachatur Nazaryan, Filippo Gaggioli, Yi Teng, Liang Fu,
• Abstract summary: We present a general and efficient method for learning the NN representation of an arbitrary many-body complex wave function.<n>Having reached overlaps as large as $99.9%$, we employ our neural wave function for pre-training to effortlessly solve the fractional quantum Hall problem.<n>Our work demonstrates efficient, accurate simulation of highly-entangled quantum matter using general-purpose deep NNs.
• Score: 0.5399800035598186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural networks (NNs) have great potential in solving the ground state of various many-body problems. However, several key challenges remain to be overcome before NNs can tackle problems and system sizes inaccessible with more established tools. Here, we present a general and efficient method for learning the NN representation of an arbitrary many-body complex wave function from its $N$-particle probability density and probability current density. Having reached overlaps as large as $99.9\%$, we employ our neural wave function for pre-training to effortlessly solve the fractional quantum Hall problem with Coulomb interactions and realistic Landau-level mixing for as many as $25$ particles. Our work demonstrates efficient, accurate simulation of highly-entangled quantum matter using general-purpose deep NNs enhanced with physics-informed initialization.

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