Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915811603513344 |
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| author | Nazaryan, Khachatur Gaggioli, Filippo Teng, Yi Fu, Liang |
| author_facet | Nazaryan, Khachatur Gaggioli, Filippo Teng, Yi Fu, Liang |
| contents | 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 and successfully test on (non-Abelian) fractional quantum Hall states and chiral BCS wavefunction. 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 and uncover distinctive features of the edge. Our work demonstrates efficient, scalable and accurate simulation of highly-entangled quantum matter using general-purpose deep NNs enhanced with physics-informed initialization. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack Nazaryan, Khachatur Gaggioli, Filippo Teng, Yi Fu, Liang Strongly Correlated Electrons Quantum Physics 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 and successfully test on (non-Abelian) fractional quantum Hall states and chiral BCS wavefunction. 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 and uncover distinctive features of the edge. Our work demonstrates efficient, scalable and accurate simulation of highly-entangled quantum matter using general-purpose deep NNs enhanced with physics-informed initialization. |
| title | Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack |
| topic | Strongly Correlated Electrons Quantum Physics |
| url | https://arxiv.org/abs/2507.13322 |