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arXiv · 2406.17252

Resource-Optimized Grouping Shadow for Efficient Energy Estimation

Min Li·Mao Lin·Matthew J. S. Beach
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Abstract

The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing. We introduce a Resource-Optimized Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer unique quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers.

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Min Li, Mao Lin, Matthew J. S. Beach. 2024-06-25. Resource-Optimized Grouping Shadow for Efficient Energy Estimation. https://doi.org/10.22331/q-2025-04-07-1694

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