Optimal short-time measurements for Hamiltonian learning

• URL: http://arxiv.org/abs/2108.08824v2
• Date: Tue, 12 Oct 2021 13:16:30 GMT
• Title: Optimal short-time measurements for Hamiltonian learning
• Authors: Assaf Zubida, Elad Yitzhaki, Netanel H. Lindner, Eyal Bairey
• Abstract summary: We propose efficient measurement schemes based on short-time dynamics. We demonstrate that the reconstruction requires a system-size independent number of experimental shots. Grouping of commuting observables and use of Hamiltonian symmetries improve the accuracy of the reconstruction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characterizing noisy quantum devices requires methods for learning the underlying quantum Hamiltonian which governs their dynamics. Often, such methods compare measurements to simulations of candidate Hamiltonians, a task which requires exponential computational complexity. Here, we propose efficient measurement schemes based on short-time dynamics which circumvent this exponential difficulty. We provide estimates for the optimal measurement schedule and reconstruction error, and verify these estimates numerically. We demonstrate that the reconstruction requires a system-size independent number of experimental shots, and identify a minimal set of state preparations and measurements which yields optimal accuracy for learning short-ranged Hamiltonians. Finally, we show how grouping of commuting observables and use of Hamiltonian symmetries improve the accuracy of the Hamiltonian reconstruction.

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