量子重载提升能谱分类精度，小深度即高效
Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity
- 用量子重载单元实现多轮数据编码，扩展频率表达能力
- 在三特征能谱任务中准确率超越单次编码基准，深度小则效果好
- 实测可在超导量子处理器上部署，适合当前硬件限制
近期量子机器学习需权衡表达力、优化难度与硬件约束。本文研究量子重载单元（QRUs）作为紧凑电路，在参数量相当条件下，对比标准单次编码变分量子电路（VQC）基线。在三特征能谱分类任务中，训练单量子比特QRU输出[-1,1]标量，并通过固定阈值映射为三类。结果表明，QRUs准确率高于基线。通过控制变量实验（深度、输入缩放、电路模板、优化器、梯度累积），发现多数增益集中于浅层，深度增加收益递减，训练成本近似线性上升。通过分析可达到的傅里叶分量，发现重复数据重编码显著扩展了每坐标频域支持范围，与随机初始化下的谱激活研究一致。最后，通过云端工作流完成模型端到端执行验证，展示其在当前硬件下的实际可部署性。
原文摘要 · Abstract (English)
Near-term quantum machine learning must balance expressivity, optimization, and hardware constraints. We study quantum re-uploading units (QRUs) as compact circuits and compare them, at matched parameter count, to a standard mono-encoded variational quantum circuit (VQC) baseline. On a three-feature calorimetry classification task, we train a single-qubit QRU that outputs a scalar in $[-1,1]$ and map it to three classes via fixed thresholds. In this setting, QRUs obtain higher accuracy than the mono-encoded baseline. A controlled ablation over depth, input scaling, circuit template, optimizer, and gradient accumulation indicates that most gains occur at small depths, with diminishing returns as depth increases while training cost grows approximately linearly. To interpret these observations, we analyze reachable Fourier components and find that repeated data re-encoding expands the per-coordinate harmonic support relative to mono-encoding, consistent with a spectral activation study over random initializations. Finally, we report an end-to-end proof-of-execution of the trained model on a superconducting QPU via a cloud workflow, illustrating practical deployability under current constraints.
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