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[2601.18047] Laser interferometry as a robust neuromorphic platform for machine learning
Summary
This paper presents a novel approach to optical neural networks using laser interferometry, enabling efficient in situ training and inference with resilience to photon losses.
Why It Matters
The research highlights a significant advancement in neuromorphic computing by leveraging linear optical resources, which could lead to more practical implementations of machine learning systems. This method addresses challenges in traditional neural networks, making it a promising area for future exploration in both optics and AI.
Key Takeaways
- Introduces a method for optical neural networks using laser interferometry.
- Enables in situ training and inference, improving experimental implementation.
- Demonstrates resilience to photon losses, enhancing reliability.
Physics > Optics arXiv:2601.18047 (physics) [Submitted on 26 Jan 2026 (v1), last revised 19 Feb 2026 (this version, v3)] Title:Laser interferometry as a robust neuromorphic platform for machine learning Authors:Amanuel Anteneh, Kyungeun Kim, J. M. Schwarz, Israel Klich, Olivier Pfister View a PDF of the paper titled Laser interferometry as a robust neuromorphic platform for machine learning, by Amanuel Anteneh and 4 other authors View PDF HTML (experimental) Abstract:We present a method for implementing an optical neural network using only linear optical resources, namely field displacement and interferometry applied to coherent states of light. The nonlinearity required for learning in a neural network is realized via an encoding of the input into phase shifts allowing for far more straightforward experimental implementation compared to previous proposals for, and demonstrations of, $\textit{in situ}$ inference. Beyond $\textit{in situ}$ inference, the method enables $\textit{in situ}$ training by utilizing established techniques like parameter shift rules or physical backpropagation to extract gradients directly from measurements of the linear optical circuit. We also investigate the effect of photon losses and find the model to be very resilient to these. Subjects: Optics (physics.optics); Emerging Technologies (cs.ET); Machine Learning (cs.LG) Cite as: arXiv:2601.18047 [physics.optics] (or arXiv:2601.18047v3 [physics.optics] for this version) https://doi.org/10.485...