2025/10/15 by Benedikt Maier, Michael Spannowsky, Maier, Benedikt +3 · 1 citation
Computer Science · #Detector #Feature selection #Gaussian #Gaussian process #Inference #Machine Learning and ELM #Neural Networks and Reservoir Computing #Perceptron #Photonics #Quantum #Quantum Information and Cryptography #Support vector machine
paper · pdf · open access · doi:10.1140/epjqt/s40507-026-00507-w
published in EPJ Quantum Technology 13(1) (Springer Science+Business Media)
openalex publication_date 2026/04/27 · openalex created_date 2026/04/28 · openalex updated_date 2026/08/05
Abstract We study continuous-variable photonic quantum extreme learning machines as fast, low-overhead front-ends for collider data processing. Data is encoded in photonic modes through quadrature displacements and propagated through a fixed-time Gaussian quantum substrate. The final readout occurs through Gaussian-compatible measurements to produce a high-dimensional random feature map. Only a linear classifier is trained, using a single logistic regression, so retraining is fast, and the optical path and detector response set the analytical and inference latency. We evaluate this architecture on two representative classification tasks, top-jet tagging and Higgs-boson identification, with parameter-matched multi-layer perceptron (MLP) baselines. Using standard public datasets and identical train, validation, and test splits, the photonic Quantum Extreme Learning Machine (QELM) outperforms an MLP with two hidden units for all considered training sizes, and matches or exceeds an MLP with ten hidden units at large sample sizes, while training only the linear readout. These results indicate that Gaussian photonic extreme-learning machines can provide compact and expressive random features at fixed latency. The combination of deterministic timing, rapid retraining, low optical power, and room temperature operation makes photonic QELMs a credible building block for online data selection and even first-stage trigger integration at future collider experiments.