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.

We are currently looking for post-docs, contact us if you are interested!

Our work featured in APL Machine Learning’s “2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review”

Our article, Classification of multi-frequency RF signals by extreme learning, using magnetic tunnel junctions as neurons and synapses by Nathan Leroux et al., is featured in the “2023 Papers with Best Practices in Data Sharing and Comprehensive Background Review” list of APL Machine Learning.

You can also read a nice Scilight (Editor’s highlight) about our work: Using spintronic devices in neural networks to analyze radio signals

Danijela Marković is awarded an ERC Starting Grant for her project qDynnet

Quantum neural networks have the potential to achieve unprecedented computational capabilities, as well as to efficiently recognize quantum states, a task inaccessible to classical computers. However, existing approaches that rely on their implementation with qubits are limited by the latter’s poor connectivity.

Danijela Marković’s project qDynnet takes a new approach, which uses parametrically coupled quantum oscillators instead of physically coupled qubits. This will allow for realization of quantum neural networks of unprecedented size, connectivity and tunability. For this, neurons are implemented as ground states of a set of coupled quantum oscillators, and connections between neurons as transitions between these states. In qDynnet project these networks will be experimentally realized with superconducting circuits and used to demonstrate automatic recognition of quantum states.

The qDynnet project will provide the understanding of physics of dynamical connections, and develop new dynamical learning methods, that will serve as a foundation for a whole new family of dynamical quantum networks.

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]