发表机构
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对机器学习与量子机器学习领域，综述混合量子神经网络的理论基础、架构、实现挑战与性能，梳理其现状并指明未来研究与应用开发的有前景路径。
AI 中文摘要
人工智能已被深度神经网络深刻改变，但新学习架构的探索仍在持续。量子机器学习提供了这一方向，混合量子神经网络结合经典神经网络组件与量子信息处理单元，成为适用于近期量子技术的实用框架。然而该领域在不同架构、基准及硬件假设下的快速发展，使得难以评估各类方案的效用、识别真正优势的产生场景，以及确定从业者如何使用这些模型。尽管近期基准测试表明此类增益尚未在大规模场景中得到验证，但理论研究已明确量子模型具备可证优势的任务，混合方法在实际问题中也取得了有前景的结果——其采用刻意精简的量子组件，且可训练参数数量大幅减少。本文针对机器学习与量子机器学习领域的研究者，对混合量子神经网络进行综述，总结其主要理论与方法基础，梳理目前已开发的部分最具前景的架构，分析其实现挑战与已报告的性能。通过整合这些视角，本综述为该领域现状提供结构化视图，助力识别未来研究与应用驱动开发的有前景路径。
英文摘要
Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.
