实证评估量子组件在混合模型中的实际贡献，发现多数情况反而降低性能。
The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment
- 通过统计实验系统测试量子与经典部分的协同作用
- 多数场景下量子组件导致模型性能下降，最佳情况才与纯经典相当
- 提醒研究者谨慎设计，避免夸大量子优势
量子机器学习作为近期量子硬件的潜在应用领域，主要依托混合量子-经典神经网络架构，融合经典与量子计算。尽管已有众多混合模型在基准任务中成功演示，但其量子组件对整体性能的具体贡献仍不明确。本文通过严谨的统计分析，系统评估常见混合模型在医疗信号数据及平面与体积分图像上的表现，考察编码方式、纠缠程度、电路规模等经典与量子因素的影响。结果显示，在最优情况下，混合模型性能可与经典模型持平；但在多数情形中，量子组件反而导致性能下降。多模态分析揭示了量子组件的真实作用，呼吁在近中期应用中对混合模型的设计持谨慎态度。
原文摘要 · Abstract (English)
Quantum machine learning has emerged as a promising application domain for near-term quantum hardware, particularly through hybrid quantum-classical models that leverage both classical and quantum processing. Although numerous hybrid architectures have been proposed and demonstrated successfully on benchmark tasks, a significant open question remains regarding the specific contribution of quantum components to the overall performance of these models. In this work, we aim to shed light on the impact of quantum processing within hybrid quantum-classical neural network architectures through a rigorous statistical study. We systematically assess common hybrid models on medical signal data as well as planar and volumetric images, examining the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. We find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts, however, in most cases, performance metrics deteriorate under the influence of quantum components. Our multi-modal analysis provides realistic insights into the contributions of quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.
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