Pré-Publication, Document De Travail Année : 2025

Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines

Malo Joly
  • Fonction : Auteur
Adrian Makowski
  • Fonction : Auteur
Baptiste Courme
Lukas Porstendorfer
  • Fonction : Auteur
Steffen Wilksen
  • Fonction : Auteur
Edoardo Charbon
  • Fonction : Auteur
Christopher Gies
  • Fonction : Auteur
Hugo Defienne
Sylvain Gigan

Résumé

Recent advancements in machine learning have led to an exponential increase in computational demands, driving the need for innovative computing platforms. Quantum computing, with its Hilbert space scaling exponentially with the number of particles, emerges as a promising solution. In this work, we implement a quantum extreme machine learning (QELM) protocol leveraging indistinguishable photon pairs and multimode fiber as a random densly connected layer. We experimentally study QELM performance based on photon coincidences -- for distinguishable and indistinguishable photons -- on an image classification task. Simulations further show that increasing the number of photons reveals a clear quantum advantage. We relate this improved performance to the enhanced dimensionality and expressivity of the feature space, as indicated by the increased rank of the feature matrix in both experiment and simulation.

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Dates et versions

hal-05424907 , version 1 (19-12-2025)

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Malo Joly, Adrian Makowski, Baptiste Courme, Lukas Porstendorfer, Steffen Wilksen, et al.. Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines. 2025. ⟨hal-05424907⟩
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