少样本语义分割中量子关联精化的受控评估：资源成本与IBM量子硬件验证
A Controlled Evaluation of Quantum Correlation Refinement for Few-Shot Semantic Segmentation: Resource Cost and IBM Quantum Hardware Validation
- Bahria University（巴瑞亚大学）
- Global College of Engineering and Technology（全球工程与技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究在少样本分割中受控评估量子关联精化器，发现其与经典MLP相比无任务级优势，但验证了硬件可部署性及资源成本。
AI中文摘要：
参数化量子电路（PQC）越来越多地被提议作为经典机器学习流程中的可训练组件，但很少在受控的任务级收益测量下进行表征。我们报告了这样一项评估，以少样本分割作为测试平台。我们将量子关联精化器（QCR）——一个具有振幅嵌入和门控残差连接的六量子比特变分电路——集成到经典的基于关联的架构中，采用四折PASCAL 5i协议，每折三个种子，与未精化的基线和匹配的MLP精化器进行比较。在12次折-种子比较中，QCR仅将mIoU改变了+0.0001（t(11)=0.10，p=0.92，dz=0.03）；MLP对照和扩展的泡利测量变体同样没有显示出改进。QCR仅增加了473个参数，但每个训练周期的训练时间大约翻倍。在IBM硬件（18个补丁）上训练的电路与无噪声模拟器非常一致（r=0.954，MAE=0.101），这是一个保真度检查，而非精度提升。该模块功能正常、可训练且可部署于硬件，但在该机制下，与匹配的经典替代方案相比，未显示出任务级优势。这是一个通过匹配对照、重复种子、资源测量和硬件验证来联合评估量子组件的模板，而非仅依赖其中任何单一方法。
英文摘要：
PQCs are increasingly proposed as trainable components in classical ML pipelines, but rarely characterized alongside a controlled measurement of task-level benefit. We report such an evaluation, using few shot segmentation as testbed. We integrate a Quantum Correlation Refiner (QCR), a six-qubit variational circuit with amplitude embedding and a gated residual connection into a classical correlation-based architecture, under the four fold PASCAL 5i protocol, three seeds per fold, against an unrefined baseline and a matched MLP refiner. Across 12 fold-seed comparisons, QCR changes mIoU by only +0.0001 (t(11)=0.10, p=0.92, dz=0.03); the MLP control and an expanded Pauli-measurement variant show similarly no improvement. QCR adds just 473 parameters but roughly doubles per epoch training time. A trained circuit on IBM hardware (18 patches) agrees closely with the noiseless simulator (r=0.954, MAE=0.101), a fidelity check, not an accuracy gain. The module is functional, trainable, and hardware deployable, yet shows no task-level advantage over a matched classical alternative under this regime, a template for evaluating quantum components jointly via matched controls, repeated seeds, resource measurement, and hardware validation, rather than any one alone.
