Team

Quantum neuromorphic computation

Scientific activity

Neuromorphic computing implements neural networks directly on physical systems, with the aim of improving their performance and energy consumption. Quantum neuromorphic computing uses quantum physical systems. This is interesting for two reasons. Firstly, the quantum state space is exponential in the size of the physical system, making it possible to obtain very large neural networks with very high performance. Secondly, such a quantum neural network has the ability to learn to automatically recognize quantum states, which are very complex to measure with classical methods.

The Quantum Neuromorphic Computing team studies quantum properties and dynamics in superconducting circuits to develop and implement new learning methods with quantum neural networks.

© Baptiste Carles, 2022.

Figure: Photos of a Josephson mixer that implements a quantum neural network.
The neurons in this network are ground states of the quantum oscillators coupled by the Josephson junction ring.