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Beer, Kerstin; Bondarenko, Dmytro; Farrelly, Terry; Osborne, Tobias J; Salzmann, Robert; Scheiermann, Daniel; Wolf, Ramona
Nature communications, 02/2020, Letnik: 11, Številka: 1Journal Article
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.
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