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http://hdl.handle.net/2445/213151
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DC Field | Value | Language |
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dc.contributor.author | Keeble, James | - |
dc.contributor.author | Drissi, Mehdi | - |
dc.contributor.author | Rojo-Francàs, Abel | - |
dc.contributor.author | Juliá-Díaz, Bruno | - |
dc.contributor.author | Ríos Huguet, Arnau | - |
dc.date.accessioned | 2024-06-14T15:14:58Z | - |
dc.date.available | 2024-06-14T15:14:58Z | - |
dc.date.issued | 2023-11-20 | - |
dc.identifier.issn | 2469-9985 | - |
dc.identifier.uri | http://hdl.handle.net/2445/213151 | - |
dc.description.abstract | We compute the ground-state properties of fully polarized, trapped, one-dimensional fermionic systems interacting through a Gaussian potential. We use an antisymmetric artificial neural network, or neural quantum state, as an Ansatz for the wave function and use machine learning techniques to variationally minimize the energy of systems from two to six particles. We provide extensive benchmarks for this toy model with other many-body methods, including exact diagonalization and the Hartree-Fock approximation. The neural quantum state provides the best energies across a wide range of interaction strengths. We find very different ground states depending on the sign of the interaction. In the nonperturbative repulsive regime, the system asymptotically reaches crystalline order. In contrast, the strongly attractive regime shows signs of bosonization. The neural quantum state continuously learns these different phases with an almost constant number of parameters and a very modest increase in computational time with the number of particles. | - |
dc.format.extent | 25 p. | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | eng | - |
dc.publisher | American Physical Society | - |
dc.relation.isformatof | Reproducció del document publicat a: https://doi.org/10.1103/PhysRevA.108.063320 | - |
dc.relation.ispartof | Physical Review C, 2023, num.108, p. 1-25 | - |
dc.relation.uri | https://doi.org/10.1103/PhysRevA.108.063320 | - |
dc.rights | (c) American Physical Society, 2023 | - |
dc.source | Articles publicats en revistes (Física Quàntica i Astrofísica) | - |
dc.subject.classification | Matèria condensada | - |
dc.subject.classification | Aprenentatge automàtic | - |
dc.subject.other | Condensed matter | - |
dc.subject.other | Machine learning | - |
dc.title | Machine learning one-dimensional spinless trapped fermionic systems with neural-network quantum states | - |
dc.type | info:eu-repo/semantics/article | - |
dc.type | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.idgrec | 743139 | - |
dc.date.updated | 2024-06-14T15:15:03Z | - |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | - |
Appears in Collections: | Articles publicats en revistes (Física Quàntica i Astrofísica) Articles publicats en revistes (Institut de Ciències del Cosmos (ICCUB)) |
Files in This Item:
File | Description | Size | Format | |
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843948.pdf | 13.1 MB | Adobe PDF | View/Open |
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