Improved machine learning algorithm for predicting ground state properties.
Nat Commun
; 15(1): 895, 2024 Jan 30.
Article
en En
| MEDLINE
| ID: mdl-38291046
ABSTRACT
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.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos