mlsanityData to 14 Oct 2026

Optimal certification of constant-local Hamiltonians

arXiv 2512.09778 ↗First posted 10 Dec 2025quant-ph cs.CC cs.DScited by 1

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Abstract (arXiv)

We study the problem of certifying local Hamiltonians from real-time access to their dynamics. Given oracle access to e-itH for an unknown k-local Hamiltonian H and a fully specified target Hamiltonian H0, the goal is to decide whether H is exactly equal to H0 or differs from H0 by at least varepsilon in normalized Frobenius norm, while minimizing the total evolution time. We introduce the first intolerant Hamiltonian certification protocol that achieves optimal performance for all constant-locality Hamiltonians. For general n-qubit, k-local, traceless Hamiltonians, our procedure uses O(ck/varepsilon) total evolution time for a universal constant c, and succeeds with high probability. In particular, for O(1)-local Hamiltonians, the total evolution time becomes Theta(1/varepsilon), matching the known Omega(1/varepsilon) lower bounds and achieving the gold-standard Heisenberg-limit scaling. Prior certification methods either relied on implementing inverse evolution of H, required controlled access to e-itH, or achieved near-optimal guarantees only in restricted settings such as the Ising case (k=2). In contrast, our algorithm requires neither inverse evolution nor controlled operations: it uses only forward real-time dynamics and achieves optimal intolerant certification for all constant-locality Hamiltonians.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 15 of its 50 references that are in the map. First posted on arXiv 10 Dec 2025.

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15 of 50 references.

References · 15

  1. 2020Sample-efficient learning of quantum many-body systemscited by 37
  2. 2022Learning many-body Hamiltonians with Heisenberg-limited scalingcited by 23
  3. 2022Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrixcited by 23
  4. 2023Learning quantum Hamiltonians at any temperature in polynomial timecited by 23
  5. 2022Practical Black Box Hamiltonian Learningcited by 16
  6. 2024Structure learning of Hamiltonians from real-time evolutioncited by 9
  7. 2024Learning k-body Hamiltonians via compressed sensingcited by 8
  8. 2024Learning the structure of any Hamiltonian from minimal assumptionscited by 5
  9. 2025Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distancescited by 5
  10. 2024Testing and learning structured quantum Hamiltonianscited by 4
  11. 2025Ansatz-free Hamiltonian learning with Heisenberg-limited scalingcited by 4
  12. 2025Hamiltonian Locality Testing via Trotterized Postselectioncited by 3
3 more
  1. 2024Simple algorithms to test and learn local Hamiltonianscited by 2
  2. 2024Efficient Hamiltonian, structure and trace distance learning of Gaussian statescited by 2
  3. 2025Certifying and learning quantum Ising Hamiltonianscited by 2

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Citing papers per year: 2026: 1.

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