量子在混合量子-经典神经网络中的作用：一项实证评估
The Role of Quantum in Hybrid Quantum-Classical Neural Networks: An Empirical Assessment
AI 导读
一项在理想无噪声仿真下的严格统计研究，系统评估了多种常用 HQNN 架构在三个二分类医学任务（医学信号、平面与体积图像）上的表现，考察编码方案、纠缠和电路规模等量子因素的影响。结果显示，最好情况下混合模型仅与纯经典模型相当，多数情况下用 QNN 组件替换匹配的经典层会使性能下降。研究呼吁对近期混合模型的应用保持谨慎的表述与设计选择。
正文 · 原文
Abstract: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 under ideal, noise-free simulation. We systematically assess a selection of commonly used HQNN architectures on three binary medical classification tasks covering medical signal data as well as planar and volumetric images. In doing so, we examine the influence attributable to classical and quantum aspects such as encoding schemes, entanglement, and circuit size. Within this experimental setting, we find that in best-case scenarios, hybrid models show performance comparable to their classical counterparts. However, in most cases, performance metrics deteriorate when the investigated QNN components replace matched classical processing layers. Our multi-modal analysis provides empirical insights into the contributions of the tested quantum components and advocates for cautious claims and design choices for hybrid models in near-term applications.
| Comments: | 16 pages, 6 figures. Accepted at QUANTICS 2026; to appear in Springer CCIS. v2: camera-ready version, claims and scope narrowed, expanded limitations; title changed from "The Role of Quantum in Hybrid Quantum-Classical Neural Networks: A Realistic Assessment" |
| Subjects: | Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2601.04732 [quant-ph] |
| (or arXiv:2601.04732v2 [quant-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2601.04732 arXiv-issued DOI via DataCite |
Submission history
From: Dominik Freinberger [view email]
[v1]
Thu, 8 Jan 2026 08:54:44 UTC (480 KB)
[v2]
Fri, 23 Oct 2026 14:47:51 UTC (466 KB)
来源：arXiv cs.AI · arxiv.org