量子机器学习需要多少重构？独立训练量子子电路的后期融合
How Much Reconstruction Does Quantum Machine Learning Need? Late Fusion of Independently Trained Quantum Subcircuits
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中文总结 AI 辅助
针对电路切割量子机器学习中重构成本过高的问题，提出后期融合方法，其准确率与完全重构差距极小但成本指数级降低，且鲁棒性更强，是重构的高效替代方案。
中文摘要 AI 辅助
电路切割技术可让大型量子神经网络（QNN）在小型设备上作为独立子电路运行，但通过重构重建其输出会产生与切割数量呈指数级增长的经典采样开销，这是现有工作中的主要运行时成本。本文针对机器学习任务，探究重构步骤是否必要，提出用后期融合替代该步骤：每个子电路独立训练和测量，再通过小型经典头部融合其输出，这是一种源自多模态学习的线性成本、决策级融合方法。为表征权衡关系，本文引入量子度Q，这是一种可调的重构预算，可在纯融合与完全重构之间插值；还提出切割-纠缠诊断指标，用于指示任务所需的重构量（104次运行的斯皮尔曼相关系数ρ=0.59）。在合成数据集和标准数据集上，独立训练的后期融合在受控扫描的每个点及所有经典基准测试中，与完全重构的准确率差距不超过0.04，但成本呈指数级降低；同时对 shots（ shots 指量子测量次数）和设备噪声的鲁棒性显著更强。受控纠缠数据实验确定了融合必然失效的边界。本文不声称量子机器学习相对于经典机器学习具有优势，符合近期基准测试结果，即量子在这些数据集上无准确率优势。因此，后期融合是电路切割量子机器学习中一种高效、噪声鲁棒且可自表征的替代重构的方案。
英文摘要
Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work. We ask whether, for machine-learning tasks, this step is necessary, and replace it with late fusion: each subcircuit is trained and measured independently, and a small classical head combines their outputs - a linear-cost, decision-level combination borrowed from multimodal learning. To characterize the trade-off we introduce a quantumness dial $Q$, a tunable reconstruction budget interpolating from pure fusion to full reconstruction, and a cut-entanglement diagnostic that indicates how much reconstruction a task needs (Spearman $ρ=0.59$ over $104$ runs). Across synthetic and standard datasets, independently trained late fusion matches full reconstruction accuracy within $0.04$ at every point of the controlled sweep and on every classical benchmark, at exponentially lower cost; it is also markedly more robust to shot and device noise. Controlled entangled-data experiments locate the boundary where fusion must fail. We do not claim advantage over classical machine learning - consistent with recent benchmarking, quantum offers no accuracy edge on these datasets. Late fusion is thus an efficient, noise-robust, self-characterizing alternative to reconstruction for circuit-cutting QML.
发表机构
- The University of Melbourne（墨尔本大学）
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