Hamiltonian Property Testing
Not on the map. Why?
Abstract (arXiv)
Locality is a fundamental feature of many physical time evolutions. Assumptions on locality and related structural properties also underlie recently proposed procedures for learning an unknown Hamiltonian from access to the induced time evolution. However, no protocols to rigorously test whether an unknown Hamiltonian is local were known. We investigate Hamiltonian locality testing as a property testing problem, where the task is to determine whether an unknown n-qubit Hamiltonian H is k-local or varepsilon-far from all k-local Hamiltonians, given access to the time evolution along H. First, we emphasize the importance of the chosen distance measure: With respect to the operator norm, a worst-case distance measure, incoherent quantum locality testers require tildeOmega(2n) many time evolution queries and an expected total evolution time of tildeOmega(2n / varepsilon), and even coherent testers need Omega(2n/2) many queries and Omega(2n/2/varepsilon) total evolution time. In contrast, when distances are measured according to the normalized Frobenius norm, corresponding to an average-case distance, we give a sample-, time-, and computationally efficient incoherent Hamiltonian locality testing algorithm based on randomized measurements. In fact, our procedure can be used to simultaneously test a wide class of Hamiltonian properties beyond locality. Finally, we prove that learning a general Hamiltonian remains exponentially hard with this average-case distance, thereby establishing an exponential separation between Hamiltonian testing and learning. Our work initiates the study of property testing for quantum Hamiltonians, demonstrating that a broad class of Hamiltonian properties is efficiently testable even with limited quantum capabilities, and positioning Hamiltonian testing as an independent area of research alongside Hamiltonian learning.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 10 of its 56 references that are in the map. First posted on arXiv 5 Mar 2024.
Builds on
10 of 56 references.
References · 10
- 2020Predicting Many Properties of a Quantum System from Very Few Measurementscited by 233
- 2021Quantum advantage in learning from experimentscited by 188
- 2022Out-of-distribution generalization for learning quantum dynamicscited by 49
- 2020Sample-efficient learning of quantum many-body systemscited by 37
- 2022Learning Quantum Processes and Hamiltonians via the Pauli Transfer Matrixcited by 23
- 2023Learning quantum Hamiltonians at any temperature in polynomial timecited by 23
- 2024Learning shallow quantum circuitscited by 20
- 2022Practical Black Box Hamiltonian Learningcited by 16
- 2023Improved Stabilizer Estimation via Bell Difference Samplingcited by 11
- 2022Lower Bounds for Learning Quantum States with Single-Copy Measurementscited by 8
Built on by
Citing papers per year: 2024: 3; 2025: 1; 2026: 2.
6 papers.
- 2024Learning k-body Hamiltonians via compressed sensingcited by 8
- 2024Learning the structure of any Hamiltonian from minimal assumptionscited by 5
- 2024Testing classical properties from quantum datacited by 2
- 2025Instance-Optimal Quantum State Certification with Entangled Measurementscited by 1
- 2026Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomographycited by 0
- 2026Hamiltonian locality testing and certification do not achieve the Heisenberg limitcited by 0
- 2026Hamiltonian locality testing and certification do not achieve the Heisenberg limit2 Oct 2026
- 2026Parameterised graph theory for tensor networks: entanglement rerouting, structural simplification, and agnostic tomography3 Sep 2026
- 2025Instance-Optimal Quantum State Certification with Entangled Measurements8 Jul 2025
- 2024Testing classical properties from quantum data19 Nov 2024
- 2024Learning the structure of any Hamiltonian from minimal assumptions29 Oct 2024
- 2024Learning k-body Hamiltonians via compressed sensing24 Oct 2024