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Beer, K.; Bondarenko, D.; Farrelly, T.; Osborne, T.J.; Salzmann, R. et al.: Training deep quantum neural networks. In: Nature Communications 11 (2020), Nr. 1, 808. DOI: https://doi.org/10.1038/s41467-020-14454-2

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Abstract: 
Neural networks enjoy widespread success in both research and industry and, with the advent of quantum technology, it is a crucial challenge to design quantum neural networks for fully quantum learning tasks. Here we propose a truly quantum analogue of classical neurons, which form quantum feedforward neural networks capable of universal quantum computation. We describe the efficient training of these networks using the fidelity as a cost function, providing both classical and efficient quantum implementations. Our method allows for fast optimisation with reduced memory requirements: the number of qudits required scales with only the width, allowing deep-network optimisation. We benchmark our proposal for the quantum task of learning an unknown unitary and find remarkable generalisation behaviour and a striking robustness to noisy training data.
License of this version: CC BY 4.0 Unported
Document Type: article
Issue Date: 2020
Appears in Collections:Fakultät für Mathematik und Physik

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