New paper on quantum reservoir computing

Our preprint on quantum reservoir computing with superconducting resonators is out! We show in simulations that quantum reservoir can be implemented on a superconducting circuit called a Josephson mixer, and that it can solve non-linearly separable machine learning tasks. We show that a smaller number of such quantum neurons can achieve the same performance as a larger number of classical neurons.

J. Dudas, E. Plouet, A. Mizrahi, J. Grollier, & D. Marković, Quantum reservoir neural network implementation on a Josephson mixer. [arXiv]

Neuromorphic physics team

Neuromorphic physics team is a part of the CNRS/Thales laboratory, associated with University Paris-Saclay. Our main research interest is neuromorphic computing with nanodevices, quantum neuromorphic computing and neuromorphic algorithms.