A new algorithm and lower bound tightly determine the total evolution time needed to test whether a Hamiltonian is local or far from local.
Simple algorithms to test and learn local Hamiltonians
1 Pith paper cite this work. Polarity classification is still indexing.
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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2025 1verdicts
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Hamiltonian Locality Testing via Trotterized Postselection
A new algorithm and lower bound tightly determine the total evolution time needed to test whether a Hamiltonian is local or far from local.