Training deep quantum neural networks
Osborne, Tobias J.
Nature Publishing Group UK
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Beer, K., Bondarenko, D., Farrelly, T., Osborne, T. J., Salzmann, R., Scheiermann, D., & Wolf, R. (2020). Training deep quantum neural networks. Nature Communications, 11 (1)https://doi.org/10.1038/s41467-020-14454-2
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.
Article, /639/705/1042, /639/766/483/481, article
Deutsche Forschungsgemeinschaft (German Research Foundation) (SFB 1227, RTG 1991, EXC 2123)
External DOI: https://doi.org/10.1038/s41467-020-14454-2
This record's URL: https://www.repository.cam.ac.uk/handle/1810/317404
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/