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Simple algorithms to test and learn local Hamiltonians

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arxiv 2404.06282 v1 pith:CUTAUH67 submitted 2024-04-09 quant-ph cs.CCcs.DScs.ITcs.LGmath.IT

classification quant-phcs.CCcs.DScs.ITcs.LGmath.IT
keywords epsilonlocalquerieshamiltonianlearningnormsimplesuffice
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abstract

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 $\epsilon_1$-close to $k$-local or $\epsilon_2$-far from $k$-local, we show that $O(1/(\epsilon_2-\epsilon_1)^{8})$ queries suffice. This solves two questions posed in a recent work by Bluhm, Caro and Oufkir. For learning up to error $\epsilon$, we show that $\exp(O(k^2+k\log(1/\epsilon)))$ queries suffice. Our proofs are simple, concise and based on Pauli-analytic techniques.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hamiltonian Locality Testing via Trotterized Postselection

    quant-ph 2025-05 conditional novelty 7.0 of 10

    A new algorithm and lower bound tightly determine the total evolution time needed to test whether a Hamiltonian is local or far from local.

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