DistributedEstimator：通过电路切割实现量子神经网络的分布式训练
DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting
AI总结：
提出DistributedEstimator，将电路切割视为分阶段分布式工作负载，通过四阶段执行管线量化切割开销、扩展限制和鲁棒性，实验表明重建阶段主导查询时间，但精度在多数配置下得以保持。
摘要：
电路切割将大型量子电路分解为独立执行的更小子电路；期望值通过经典组合子电路结果来恢复。先前的工作通过子电路数量和采样复杂度来表征切割开销，但其对迭代式、基于估计器的训练管线的端到端影响从系统角度仍未被充分衡量。我们提出DistributedEstimator，一个切割感知的估计器执行管线，将电路切割视为分阶段分布式工作负载，将每次查询分为四个阶段：分区、子实验生成、并行执行和经典重建。利用两个二分类工作负载（Iris和MNIST）的运行时跟踪记录和学习结果，我们量化了切割开销、扩展限制以及对注入掉队者的敏感性，并评估在匹配训练预算下是否保持了准确性和鲁棒性。重建主导每次查询时间——三次切割时中位数为53%，第95百分位为58%——限制了并行性下可实现的速度提升。尽管如此，在Iris上测试精度完全保持，在MNIST上所有切割配置下均未出现系统性退化。在高斯噪声和FGSM扰动下的鲁棒性同样保持，几种配置匹配或优于未切割基线。子实验数量的指数增长（基于CNOT分解为${O}(9^c)$）是基本障碍，将实际实验限制在小量子比特数。这些结果表明，学习工作负载的实际扩展需要减少和重叠重建、针对屏障主导关键路径的调度策略，以及针对更大量子比特数的计算高效重建策略。
英文摘要：
Circuit cutting decomposes a large quantum circuit into smaller subcircuits executed independently; expectation values are recovered by classically combining subcircuit outcomes. Prior work characterises cutting overhead via subcircuit counts and sampling complexity, but its end-to-end impact on iterative, estimator-driven training pipelines remains under-measured from a systems perspective. We propose DistributedEstimator, a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload, instrumenting each query across four phases: partitioning, subexperiment generation, parallel execution, and classical reconstruction. 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 assess whether accuracy and robustness are preserved under matched training budgets. Reconstruction dominates per-query time -- a median of 53% and 95th percentile of 58% at three cuts -- bounding achievable speed-up under parallelism. Despite this, test accuracy is fully preserved on Iris and maintained without systematic degradation on MNIST across all cut configurations. Robustness under Gaussian noise and FGSM perturbations is similarly preserved, with several configurations matching or improving on the uncut baseline. Exponential growth of subexperiment counts (${O}(9^c)$ for CNOT-based decomposition) is a fundamental barrier limiting practical experimentation to small qubit counts. These results establish that practical scaling for learning workloads requires reducing and overlapping reconstruction, scheduling policies for barrier-dominated critical paths, and computationally efficient reconstruction strategies for larger qubit counts.
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