Circuit cutting decomposes a large quantum circuit into a collection of smaller subcircuits. The outputs of these subcircuits are then classically reconstructed to recover the original expectation values. While prior work characterises cutting overhead largely in terms of subcircuit counts and sampling complexity, its end-to-end impact on iterative, estimator-driven training pipelines remains insufficiently measured from a systems perspective. In this paper, we propose a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload and instruments each estimator query into partitioning, subexperiment generation, parallel execution, and classical reconstruction phases. Using logged runtime traces and learning outcomes on two binary classification workloads (Iris and MNIST), we quantify cutting overheads, scaling limits, and sensitivity to injected stragglers, and we evaluate whether accuracy and robustness are preserved under matched training budgets. Our measurements show that cutting introduces substantial end-to-end overheads that grow with the number of cuts, and that reconstruction constitutes a dominant fraction of per-query time, bounding achievable speed-up under increased parallelism. Despite these systems costs, test accuracy and robustness are preserved in the measured regimes, with configuration-dependent improvements observed in some cut settings. These results indicate that practical scaling of circuit cutting for learning workloads hinges on reducing and overlapping reconstruction and on scheduling policies that account for barrier-dominated critical paths.

翻译：电路切割将大型量子电路分解为多个较小的子电路集合。随后通过经典计算重构这些子电路的输出，以恢复原始期望值。尽管已有研究主要从子电路数量和采样复杂度角度刻画切割开销，但从系统视角衡量其对迭代式、估计器驱动的训练流程的端到端影响仍显不足。本文提出一种支持切割的估计器执行流程，将电路切割视为分阶段的分布式工作负载，并将每个估计器查询分解为划分、子实验生成、并行执行和经典重构四个阶段。基于两个二分类任务（Iris和MNIST）的运行日志与学习结果，我们量化了切割开销、扩展极限及对注入延迟的敏感性，并评估了在匹配训练预算下精度与鲁棒性是否得以保持。实验结果表明：切割会引入显著的端到端开销，且随切割数量增加而增长；重构阶段占据单次查询时间的主导部分，限制了并行化提升的速度上限。尽管存在这些系统开销，在测试范围内精度与鲁棒性得以保持，且在某些切割配置下观察到依赖配置的性能提升。这些结果表明：电路切割在学习任务中的实际扩展能力，关键在于减少和重叠重构操作，并采用能应对以同步屏障为主导的关键路径的调度策略。