mlsanityData to 7 Oct 2026

Improved machine learning algorithm for predicting ground state properties

arXiv 2301.13169 ↗First posted 30 Jan 2023quant-ph cs.LG physics.comp-phcited by 22

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Abstract (arXiv)

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 O(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require O(nc) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as O(n log n) 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.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 22 of its 104 references that are in the map. First posted on arXiv 30 Jan 2023.

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22 of 104 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
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10 more
  1. 2021Exponential separations between learning with and without quantum memorycited by 50
  2. 2016Discovering Phase Transitions with Unsupervised Learningcited by 49
  3. 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
  4. 2022Learning to predict arbitrary quantum processescited by 34
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  7. 2021Quantum Algorithms and Lower Bounds for Linear Regression with Norm Constraintscited by 11
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  9. 2022Predicting Properties of Quantum Systems with Conditional Generative Modelscited by 8
  10. 2022Hardware-efficient learning of quantum many-body statescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 7; 2025: 7; 2026: 4.

22 papers.

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  1. 2024A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerancecited by 32
  2. 2023A survey on the complexity of learning quantum statescited by 31
  3. 2023Multimodal deep representation learning for quantum cross-platform verificationcited by 9
  4. 2024Equivariant Symmetry Breaking Setscited by 9
  5. 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
  6. 2023Exponentially improved efficient machine learning for quantum many-body states with provable guaranteescited by 7
  7. 2024Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuitscited by 7
  8. 2025Rethink the Role of Deep Learning towards Large-scale Quantum Systemscited by 4
  9. 2023Efficient Learning of Long-Range and Equivariant Quantum Systemscited by 3
  10. 2024Concept learning of parameterized quantum models from limited measurementscited by 3
  11. 2024Efficient Hamiltonian, structure and trace distance learning of Gaussian statescited by 2
  12. 2024Accurate Learning of Equivariant Quantum Systems from a Single Ground Statecited by 1
10 more
  1. 2025Sequence-Model-Guided Measurement Selection for Quantum State Learningcited by 1
  2. 2025Quantum circuit complexity and unsupervised machine learning of topological ordercited by 1
  3. 2025Learning quantum many-body data locally: A provably scalable frameworkcited by 1
  4. 2025Nonstabilizerness Estimation using Graph Neural Networkscited by 1
  5. 2026Parametric Quantum State Tomography with HyperRBMscited by 1
  6. 2024Classical Shadows with Improved Median-of-Means Estimationcited by 0
  7. 2025Learning thermodynamic master equations for open quantum systemscited by 0
  8. 2026Physical probes expose and alleviate chemical-environment collapse in molecular representationscited by 0
  9. 2026Learning ground state observables from quantum computing experimentscited by 0
  10. 2026A Quantum/Classical Example Oracle Separation for Making Things Upcited by 0