[2603.09645] Noise in Photonic Quantum Machine Learning: Models, Impacts, and Mitigation Strategies

Quantum Physics arXiv:2603.09645 (quant-ph) [Submitted on 10 Mar 2026 (v1), last revised 30 Mar 2026 (this version, v2)] Title:Noise in Photonic Quantum Machine Learning: Models, Impacts, and Mitigation Strategies Authors:A.M.A.S.D. Alagiyawanna, Asoka Karunananda View a PDF of the paper titled Noise in Photonic Quantum Machine Learning: Models, Impacts, and Mitigation Strategies, by A.M.A.S.D. Alagiyawanna and Asoka Karunananda View PDF HTML (experimental) Abstract:Photonic Quantum Machine Learning (PQML) is an emerging method to implement scalable, energy-efficient quantum information processing by combining photonic quantum computing technologies with machine learning techniques. The features of photonic technologies offer several benefits: room-temperature operation; fast (low delay) processing of signals; and the possibility of representing computations in high-dimensional (Hilbert) spaces. This makes photonic technologies a good candidate for the near-term development of quantum devices. However, noise is still a major limiting factor for the performance, reliability, and scalability of PQML implementations. This review provides a detailed and systematic analysis of the sources of noise that will affect PQML implementations. We will present an overview of the principal photonic quantum computer designs and summarize the many different types of quantum machine learning algorithms that have been successfully implemented using photonic quantum computer architectures suc...