Hamiltonian locality testing and certification do not achieve the Heisenberg limit
quant-ph, cs.CC, cs.DS, cs.LG
Submitted: 2026-10-02
Updated: 2026-10-09
Comments: 28 pages
License: http://creativecommons.org/licenses/by/4.0/
The gist: We establish lower bounds for Hamiltonian property testing with access to the time-evolution operator but not its inverse.
Abstract
We establish lower bounds for Hamiltonian property testing with access to the time-evolution operator but not its inverse. Each experiment may query the time-evolution operator multiple times, and distances between Hamiltonians are measured in the normalized Frobenius norm. In this model, we show that testing whether a Hamiltonian is k-local or epsilon-far from every k-local Hamiltonian requires Ω(1/epsilon 2) total evolution time, matching the upper bound of Kallaugher and Liang (TQC'25). We also prove that testing whether an unknown Hamiltonian equals a target Hamiltonian or is epsilon-far from it requires Ω(1/epsilon 2) total evolution time, matching the upper bound of Sinha and Tong (2025). These are the first lower bounds for natural problems in Hamiltonian learning and testing that rule out Heisenberg-limited scaling of 1/epsilon. As a third result, we show that amplitude estimation to precision epsilon requires Ω(1/epsilon 2) total time evolution, recovering the result of Tang and Wright (QIP'26) in the continuous-time query model. All three results follow from the hardness of distinguishing the zero Hamiltonian from a suitably chosen ensemble of random Hamiltonians. We establish this hardness by adapting the continuous-time adversary method to forward Hamiltonian evolution.
Sources
- Nearly optimal algorithms to learn sparse quantum Hamiltonians in physically motivated distances
- Near-Optimal Learning of Local Lindbladians
- Certifying and learning local quantum Hamiltonians
- Variations on Quantum Adversary
- Optimal detection of dissipation in Lindbladian dynamics
- Lower Bounds for Learning Hamiltonians from Time Evolution
- Autonomous Hamiltonian certification and changepoint detection
- Optimal classical shadow estimation of unitary channels at Heisenberg limit
- Learning the structure of open quantum systems
- Robust Structure Learning of k-local Lindbladians
- Learning Arbitrary Lindbladians with Quantum Error Correction
- Emergent Prethermal Symmetries for Scalable Hamiltonian Learning
- Heisenberg-limited Hamiltonian learning without short-time control
- Hamiltonian Learning and Certification via Eigenphase Engineering
- Amplitude amplification and estimation require inverses
- Characterizing Arbitrary Lindbladian Dynamics with a Few Pauli Measurements
- Optimal Ansatz-free Hamiltonian Learning In Situ
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