Structure learning of Hamiltonians from real-time evolution
Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang

TL;DR
This paper introduces a new Hamiltonian learning algorithm that efficiently recovers unknown local Hamiltonians from real-time evolution, achieving Heisenberg-limited scaling without prior knowledge of interaction structure.
Contribution
The authors develop a general Hamiltonian learning method that extends to complex interaction structures and achieves optimal scaling, surpassing previous limitations.
Findings
Algorithm recovers Hamiltonian with error ε in time O(log(n)/ε)
Method works beyond short-range interactions, including power-law decay Hamiltonians
Achieves constant time resolution and beats standard error scaling limits
Abstract
We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply for an unknown local Hamiltonian on qubits, the goal is to recover . This problem is already well-understood under the assumption that the interaction terms, , are given, and only the interaction strengths, , are unknown. But how efficiently can we learn a local Hamiltonian without prior knowledge of its interaction structure? We present a new, general approach to Hamiltonian learning that not only solves the challenging structure learning variant, but also resolves other open questions in the area, all while achieving the gold standard of Heisenberg-limited scaling. In particular, our algorithm recovers the Hamiltonian to error with total evolution time , and…
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