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Quantum Machine Learning

QSRA: A QPU Scheduling and Resource Allocation Approach for Cloud-Based Quantum Computing

arXiv
Authors: Binhan Lu, Zhaoyun Chen, Yuchun Wu

Year

2024

Paper ID

37005

Status

Preprint

Abstract Read

~2 min

Abstract Words

96

Citations

N/A

Abstract

Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a QPU Scheduling and Resource Allocation (QSRA) approach to address these challenges. QSRA enhances qubit utilization and reduces turnaround time by adapting CPU scheduling techniques to Quantum Processing Units (QPUs). It incorporates a subroutine for qubit allocation that takes into account qubit quality and connectivity, while also merging multiple quantum programs to further optimize qubit usage. Our evaluation of QSRA against existing methods demonstrates its effectiveness in improving both qubit utilization and turnaround time.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2411.05283 [2] arXiv https://arxiv.org/abs/2411.05283 [3] Publisher https://arxiv.org/abs/2411.05283

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