发表机构
Newcastle University; National Quantum Computing Centre; University of Cambridge（纽卡斯尔大学; 国家量子计算中心; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究发现Qiskit Machine Learning的SamplerQNN后处理存在数据丢失问题，导致量子神经网络推理准确率下降、训练损失信号压缩，提出的修复方案已合并入GitHub代码库。
AI 中文摘要
随着量子硬件扩展至更大规模的器件，与其对接的经典软件层必须同步演进。主要在模拟器环境中开发和测试的后处理例程可能会编码一些在实用级器件上不再成立的假设，导致仅从高层模型输出难以检测到的数据丢失。本文对Qiskit Machine Learning库中基于采样的量子神经网络类SamplerQNN进行了案例研究。该后处理方法应用了一个假设测量比特串处于虚拟量子比特空间的过滤器。在我们的量子硬件运行中，比特串跨越了100多个物理量子比特，该过滤器导致85%至99.6%的有效测量 shots 丢失，具体取决于 transpiler 的量子比特布局。生成的概率向量未归一化，使得扭曲的预测和损失值可在无API级警告的情况下在模型中传播。我们在两个IBM后端上的五项实验中展示了这种影响：对于推理，在相同原始测量数据上准确率从0.94降至0.39；对于训练，损失信号被压缩了22至27倍，大幅降低了优化器对目标景观的敏感性。该行为出现在该库的所有发布版本（0.8.4至0.9.0）中。我们实现了一种基于布局的边缘化修复方案，作为拉取请求#1041合并到GitHub代码库中，该方案使SamplerQNN后处理与当前及未来硬件向前兼容。
英文摘要
As quantum hardware scales to larger devices, the classical software layers that interface with it must evolve in step. Postprocessing routines developed and tested primarily in simulator settings can encode assumptions that no longer hold on utility-scale devices, leading to data loss that can be difficult to detect from high-level model outputs alone. We present a case study of \texttt{SamplerQNN}, the sampling-based quantum neural network class in the Qiskit Machine Learning library. Here, the postprocessing method applies a filter that assumes measurement bit-strings are in virtual qubit space. On our quantum hardware runs, where bit-strings span over 100 physical qubits, this filter led to the loss of 85 to 99.6\% of valid measurement shots, depending on the transpiler's qubit placement. The resulting probability vector is unnormalised, allowing distorted prediction and loss values to propagate through the model without an API-level warning. We demonstrate the impact across five experiments on two IBM backends: for inference, accuracy drops from 0.94 to 0.39 on the same raw measurements; for training, the loss signal is compressed by 22 to 27$\times$, substantially reducing the sensitivity of the optimiser to the objective landscape. The behaviour arises in all released versions of the library (0.8.4 to 0.9.0). We implemented a layout-based marginalisation fix, merged into the GitHub codebase as Pull Request \#1041, that makes \texttt{SamplerQNN} postprocessing forward-compatible with current and upcoming hardware.
Comments8 pages, one figure
