Training deep quantum neural networks

Kerstin Beer (), Dmytro Bondarenko, Terry Farrelly, Tobias J. Osborne, Robert Salzmann, Daniel Scheiermann and Ramona Wolf
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Kerstin Beer: Institut für Theoretische Physik, Leibniz Universität Hannover
Dmytro Bondarenko: Institut für Theoretische Physik, Leibniz Universität Hannover
Terry Farrelly: Institut für Theoretische Physik, Leibniz Universität Hannover
Tobias J. Osborne: Institut für Theoretische Physik, Leibniz Universität Hannover
Robert Salzmann: Institut für Theoretische Physik, Leibniz Universität Hannover
Daniel Scheiermann: Institut für Theoretische Physik, Leibniz Universität Hannover
Ramona Wolf: Institut für Theoretische Physik, Leibniz Universität Hannover

Nature Communications, 2020, vol. 11, issue 1, 1-6

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-14454-2

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