Objective Quantum neural networks are a young research field, that has been rapidly expanding due to their potential to attain revolutionary computing capacities and the possibility to learn on quantum data, inaccessible to classical computers. However, despite impressive proof-of-concept results, currently existing approaches that rely on sparsely coupled qubits, are not scalable to network sizes and connectivities with tunable weights required for state-of-the art tasks. In qDynnet, I will adopt a completely new and unexplored approach that uses parametrically coupled superconducting quantum oscillators instead of physically coupled qubits, that will allow me to obtain quantum neural networks of unprecedented size, connectivity and tunability. To do this, I will shift the paradigm by implementing neurons as basis states of dynamically coupled multi-level quantum oscillators, and connections between neurons as transition rates obtained through different dynamical processes such as parametric coupling, resonant drives and dissipation. I will implement experimentally quantum neural network architectures that were only simulated until now and use them to demonstrate data classification with basis state neurons. In order to go towards more complex tasks, I will use parametric coupling to introduce tunable connections between neurons. I will develop new training methods that will allow me to tune connections in such dynamical quantum neural networks and use them to demonstrate learning to recognize quantum states. I will develop circuit geometries that will be scalable to large quantum neural networks with millions of neurons and tunable connections. The qDynnet project will provide understanding of physics, and methods for dynamical coupling and training, that will have a broad impact across quantum computing fields and serve as a foundation for a whole new family of large-scale dynamical quantum neural networks. Fields of science engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computersnatural sciencesmathematicspure mathematicsgeometrynatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Keywords quantum machine learning neuromorphic computing superconducting circuits Programme(s) HORIZON.1.1 - European Research Council (ERC) Main Programme Topic(s) ERC-2022-STG - ERC STARTING GRANTS Call for proposal ERC-2022-STG See other projects for this call Funding Scheme ERC - Support for frontier research (ERC) Coordinator CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS Net EU contribution € 1 497 536,00 Address Rue michel ange 3 75794 Paris France See on map Region Ile-de-France Ile-de-France Paris Activity type Research Organisations Links Contact the organisation Opens in new window Website Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 0,00
