Improved machine learning algorithm for predicting ground state properties.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 Jan 2024
30 Jan 2024
Historique:
received:
26
03
2023
accepted:
08
01
2024
medline:
31
1
2024
pubmed:
31
1
2024
entrez:
30
1
2024
Statut:
epublish
Résumé
Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.
Identifiants
pubmed: 38291046
doi: 10.1038/s41467-024-45014-7
pii: 10.1038/s41467-024-45014-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
895Informations de copyright
© 2024. The Author(s).
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