Noise Models Impacts and Mitigation Strategies in Photonic Quantum Machine Learning
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 ...
| Authors | A. M. A. S. D. Alagiyawanna, Asoka Karunananda |
|---|---|
| Primary category | quant-ph |
| Published | 10 March 2026 |
| Last updated at source | 4 April 2026 |
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