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Trapped Ion Quantum Computing

Resource-Optimized Grouping Shadow for Efficient Energy Estimation

arXiv
Authors: Min Li, Mao Lin, Matthew J. S. Beach

Year

2024

Paper ID

66130

Status

Preprint

Abstract Read

~2 min

Abstract Words

87

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2406.17252 [2] arXiv https://arxiv.org/abs/2406.17252 [3] Publisher https://arxiv.org/abs/2406.17252

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