mlsanityData to 7 Oct 2026

Simple algorithms to test and learn local Hamiltonians

arXiv 2404.06282 ↗First posted 9 Apr 2024quant-ph cs.CC cs.DScited by 2

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Abstract (arXiv)

We consider the problems of testing and learning an n-qubit k-local Hamiltonian from queries to its evolution operator with respect the 2-norm of the Pauli spectrum, or equivalently, the normalized Frobenius norm. For testing whether a Hamiltonian is ε1-close to k-local or ε2-far from k-local, we show that O(1/(ε2-ε1)8) queries suffice. This solves two questions posed in a recent work by Bluhm, Caro and Oufkir. For learning up to error ε, we show that exp(O(k2+klog(1/ε))) queries suffice. Our proofs are simple, concise and based on Pauli-analytic techniques.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

7 of 23 references.

References · 7

  1. 2020Sample-efficient learning of quantum many-body systemscited by 36
  2. 2022Learning to predict arbitrary quantum processescited by 34
  3. 2022Learning many-body Hamiltonians with Heisenberg-limited scalingcited by 22
  4. 2022Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrixcited by 22
  5. 2022Practical Black Box Hamiltonian Learningcited by 15
  6. 2021Learning low-degree functions from a logarithmic number of random queriescited by 8
  7. 2022Scalably learning quantum many-body Hamiltonians from dynamical datacited by 7

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Citing papers per year: 2024: 1; 2025: 1.

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