ArticleNature communications2024
Improved machine learning algorithm for predicting ground state properties.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Efficient Hamiltonian, structure and trace distance learning of Gaussian states.Nature communications · 2026Article
- Quantum circuit complexity and unsupervised machine learning of topological order.Nature communications · 2026Article
- Exponential quantum advantages in learning quantum observables from classical data.NPJ quantum information · 2026Article
- A modular artificial intelligence framework to facilitate fluorophore design.Nature communications · 2025Article
- Unveiling quantum phase transitions from traps in variational quantum algorithms.NPJ quantum information · 2025Article
- Efficient learning of ground and thermal states within phases of matter.Nature communications · 2024Article
- Machine learning on quantum experimental data toward solving quantum many-body problems.Nature communications · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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