quant-phMar 10, 2026

Noise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning

Authors: A. M. A. S. D. Alagiyawanna, Asoka Karunananda

Organizations: Department of Computational Mathematics, University of Moratuwa, Sri Lanka

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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 such as variational quantum circuits, quantum neural networks, and quantum support vector machines. We identify and categorize the primary sources of noise within photonic quantum systems and how these sources of noise behave algorithm-specifically with respect to degrading the accuracy of learning, unstable training, and slower convergence than expected. Additionally, we review traditional and advanced techniques for characterizing noise and provide an extensive survey of strategies for mitigating the effects of noise on learning performance. Finally, we discuss recent advances that demonstrate PQML's capability to operate in real-world settings with realistic noise conditions and future obstacles that will challenge the use of PQML as an effective quantum processing platform.

Figures & tables

Figure 1: Timeline of major advancements in quantum machine learning.
Figure 2: Schematic overview of a photonic quantum computing system showing optical input processing through a photonic chip and measurement via detectors with classical control electronics.
Figure 3: Schematic illustration of a single photon interacting with a beam splitter and being detected at two output ports.
Figure 4: Schematic illustration of key components in hybrid quantum systems, including continuous-variable elements such as electromagnetic fields and mechanical oscillators, discrete-variable spin ensembles, phase-space representation via the Wigner function, and homodyne detection.
Figure 5: Schematic representation of a variational quantum circuit showing data embedding, parametrized quantum evolution, measurement, and classical post-processing.
Figure 6: Schematic illustration of machine learning-based quantum error mitigation where a trained model learns to map noisy expectation values obtained from a quantum circuit to noise-mitigated predictions through supervised training.
Figure 7: System-level schematic of an integrated programmable photonic quantum processor, showing the interaction between a classical control interface, control electronics, photonic chip, optical components, and detection system, with feedback for system optimization.
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