Quantum neuromorphic computing implements neural networks on quantum physical systems. This is interesting for two reasons. First, the size of the quantum state space is exponential in the size of the physical system, which allows to obtain very large neural networks of very high computing capacity.  Second, such quantum neural network has the capacity to process input quantum states and learn to automatically recognize them. This could have a huge impact on the field of quantum computing, as such states can be extremely complex to measure with classical methods.

In our team, we study quantum properties and dynamics in superconducting circuits in order to develop and implement new learning methods with the quantum neural networks. On this topic we collaborate closely with QCMX team at Ecole Polytechnique.