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Bibliographic Details
Main Authors: Li, Min, Lin, Mao, Beach, Matthew J. S.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2406.17252
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author Li, Min
Lin, Mao
Beach, Matthew J. S.
author_facet Li, Min
Lin, Mao
Beach, Matthew J. S.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2406_17252
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Resource-Optimized Grouping Shadow for Efficient Energy Estimation
Li, Min
Lin, Mao
Beach, Matthew J. S.
Quantum Physics
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.
title Resource-Optimized Grouping Shadow for Efficient Energy Estimation
topic Quantum Physics
url https://arxiv.org/abs/2406.17252