Noise, Quantum Transport & Machine Learning

This research group works mainly on noisy quantum systems, where the interplay of entanglement and noise plays a crucial role in the performance of quantum systems, e.g. in the transport of energy, matter or classical/quantum information over complex networks/topologies or biological structures. These studies involve both fundamental and applied research and indeed are often in collaboration with experimental groups in order to test the achieved theoretical predictions on atomic and photonic platforms. They often have also technological applications for quantum sensing, communication and computation. Finally, this group is strongly involved in the very recent but rapidly growing field of quantum machine learning combining quantum information theory with machine learning algorithms where classical/quantum data can train a learning model that is based on classical/quantum algorithms. These analyses pave the way for new experimental demonstrations of hybrid classical-quantum machine learning protocols allowing to evaluate their potential advantages over their classical counterparts by exploiting the already available noisy intermediate scale quantum devices.

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