Training deep quantum neural networks

• Published in: Nature communications Vol. 11; no. 1; p. 808
• Main Authors: Beer, Kerstin, Bondarenko, Dmytro, Farrelly, Terry, Osborne, Tobias J., Salzmann, Robert, Scheiermann, Daniel, Wolf, Ramona
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.02.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 639/705/1042; 639/766/483/481; Artificial neural networks; Cognitive tasks; Cost function; Humanities and Social Sciences; Learning; multidisciplinary; Neural networks; Neurons; Optimization; Quantum computing; Science; Science (multidisciplinary); Training
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-020-14454-2

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. It is hard to design quantum neural networks able to work with quantum data. Here, the authors propose a noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers.