Photonic Quantum Machine Learning (PQML) is an emerging approach that integrates photonic quantum computing technologies with machine learning techniques to enable scalable and energy-efficient quantum information processing. Photonic systems offer advantages such as room-temperature operation, high-speed signal processing, and the ability to represent information in high-dimensional Hilbert spaces. However, noise remains a major challenge affecting the performance, reliability, and scalability of PQML implementations. This review provides a systematic analysis of noise sources in photonic quantum machine learning systems. We discuss photonic quantum computing architectures and examine key quantum machine learning algorithms implemented on photonic platforms, including Variational Quantum Circuits, Quantum Neural Networks, and Quantum Support Vector Machines. The paper categorizes major noise mechanisms and analyzes their impact on learning performance, training stability, and convergence behavior. Furthermore, we review both traditional and advanced noise characterization techniques and survey recent strategies for noise mitigation in photonic quantum systems. Finally, we highlight recent experimental advances and discuss future research directions for developing robust and scalable PQML systems under realistic noise conditions.

翻译：光子量子机器学习（PQML）是一种新兴方法，它将光子量子计算技术与机器学习技术相结合，以实现可扩展且节能的量子信息处理。光子系统具有室温操作、高速信号处理以及在希尔伯特高维空间中表示信息的能力等优势。然而，噪声仍然是影响PQML实现性能、可靠性和可扩展性的主要挑战。本综述系统分析了光子量子机器学习系统中的噪声源。我们讨论了光子量子计算架构，并研究了在光子平台上实现的关键量子机器学习算法，包括变分量子电路、量子神经网络和量子支持向量机。本文对主要噪声机制进行了分类，并分析了它们对学习性能、训练稳定性和收敛行为的影响。此外，我们回顾了传统和先进的噪声表征技术，并综述了光子量子系统中噪声缓解的最新策略。最后，我们重点介绍了最近的实验进展，并讨论了在现实噪声条件下开发鲁棒且可扩展的PQML系统的未来研究方向。