Resource-Optimized Grouping Shadow for Efficient Energy Estimation

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 erro...

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Main Authors: Min Li, Mao Lin, Matthew J. S. Beach
Format: Article
Language:English
Published: Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften 2025-04-01
Series:Quantum
Online Access:https://quantum-journal.org/papers/q-2025-04-07-1694/pdf/
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author Min Li
Mao Lin
Matthew J. S. Beach
author_facet Min Li
Mao Lin
Matthew J. S. Beach
author_sort Min Li
collection DOAJ
description 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 Article
id doaj-art-d4d4e6097b9a46f69aeb9d6af5955aa0
institution OA Journals
issn 2521-327X
language English
publishDate 2025-04-01
publisher Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
record_format Article
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spelling doaj-art-d4d4e6097b9a46f69aeb9d6af5955aa02025-08-20T01:53:23ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2025-04-019169410.22331/q-2025-04-07-169410.22331/q-2025-04-07-1694Resource-Optimized Grouping Shadow for Efficient Energy EstimationMin LiMao LinMatthew J. S. BeachThe 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.https://quantum-journal.org/papers/q-2025-04-07-1694/pdf/
spellingShingle Min Li
Mao Lin
Matthew J. S. Beach
Resource-Optimized Grouping Shadow for Efficient Energy Estimation
Quantum
title Resource-Optimized Grouping Shadow for Efficient Energy Estimation
title_full Resource-Optimized Grouping Shadow for Efficient Energy Estimation
title_fullStr Resource-Optimized Grouping Shadow for Efficient Energy Estimation
title_full_unstemmed Resource-Optimized Grouping Shadow for Efficient Energy Estimation
title_short Resource-Optimized Grouping Shadow for Efficient Energy Estimation
title_sort resource optimized grouping shadow for efficient energy estimation
url https://quantum-journal.org/papers/q-2025-04-07-1694/pdf/
work_keys_str_mv AT minli resourceoptimizedgroupingshadowforefficientenergyestimation
AT maolin resourceoptimizedgroupingshadowforefficientenergyestimation
AT matthewjsbeach resourceoptimizedgroupingshadowforefficientenergyestimation