AI 中文总结
针对真实有噪声中等规模量子硬件局限，提出统一框架，通过两阶段协议、量子多编程等方法在IBM Eagle处理器上对十类MNIST端到端分类，展示多类量子图像分类可行性及工作流程，虽无参数准确率优势但推动了相关研究。
AI 中文摘要
在真实的有噪声中等规模量子（NISQ）硬件上进行量子机器学习，很大程度上局限于二进制或少数类任务，受硬件训练成本和推理时大型设备未充分利用的限制。我们提出一个统一框架，在127量子比特的IBM Eagle处理器上对十类MNIST进行端到端分类，有三个主要贡献。一是两阶段协议将编码器和读出的基于梯度的经典优化与量子参数的无梯度优化解耦，消除使硬件训练不切实际的参数移位梯度成本。二是首次将量子多编程引入训练好的量子分类器，在一个设备上打包多个电路副本以实现并行推理且不降低平均准确率，同时按比例减少量子处理器作业提交。三是对比表明硬件微调无显著准确率提升，推动实用的NISQ工作流程：在经典模拟器上训练，仅将硬件用于推理。与匹配容量的经典网络对比，该量子模块在此规模下无参数准确率优势，因此将此工作视为当前硬件上多类量子图像分类的可行性和工作流程演示。
英文摘要
Quantum machine learning on real noisy intermediate-scale quantum (NISQ) hardware has remained largely confined to binary or few-class tasks, limited by the cost of on-hardware training and the underuse of large devices at inference. We present a unified framework that classifies ten-class MNIST end-to-end on a $127$-qubit IBM Eagle processor, with three central contributions. First, a two-phase protocol decouples a gradient-based classical optimization of the encoder and readout from a gradient-free optimization of the quantum parameters, removing the parameter-shift gradient cost that makes on-hardware training impractical. Second, we introduce Quantum Multi-Programming to a trained quantum classifier for the first time, packing multiple circuit copies onto one device to deliver parallel inference at no mean-accuracy cost while cutting quantum-processor job submissions proportionally. Third, a controlled comparison shows that on-hardware fine-tuning yields no measurable accuracy gain, motivating a practical NISQ workflow: train on a classical simulator and reserve the hardware for inference only. Benchmarked against a matched-capacity classical network, the quantum module shows no per-parameter accuracy advantage at this scale; we therefore frame the work as a feasibility-and-workflow demonstration for multi-class quantum image classification on current hardware.
