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The qDynnet project explores a novel approach to quantum neural networks by utilizing parametrically coupled superconducting quantum oscillators. This method aims to create scalable networks with enhanced connectivity and tunability, enabling advanced data classification and learning tasks in quantum computing.
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 compl…
CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS
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