← 研踪 PaperTrailarXiv:2412.17704 · v4
ShotQC: Reducing Sampling Overhead in Quantum Circuit Cutting
首次提交 2024-12-24 00:34
版本更新 2026-10-28 16:25
雷达观测 2026-10-30 10:22
摘要
The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In this work, we propose an enhanced circuit cutting framework, ShotQC, which effectively reduces the sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo strategy to dynamically allocate more quantum shots to subcircuit configurations that contribute more to the variance in the final outcome. The latter exploits additional degrees of freedom in postprocessing to further suppress variance. Integrating these optimizations, ShotQC significantly reduces the sampling overhead without increasing classical postprocessing complexity, as demonstrated across a range of benchmark circuits.
作者备注：58 pages, 29 figures, 8 tables; v4: more benchmarks and analyses added, version to appear in Advanced Quantum Technologies
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v4 · 2026-10-28 16:25 · 最新观测版本
ShotQC: Reducing Sampling Overhead in Quantum Circuit Cutting
The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In this work, we propose an enhanced circuit cutting framework, ShotQC, which effectively reduces the sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo strategy to dynamically allocate more quantum shots to subcircuit configurations that contribute more to the variance in the final outcome. The latter exploits additional degrees of freedom in postprocessing to further suppress variance. Integrating these optimizations, ShotQC significantly reduces the sampling overhead without increasing classical postprocessing complexity, as demonstrated across a range of benchmark circuits.
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