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Training deep quantum neural networks.

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.

SUBMITTER: Beer K

PROVIDER: S-EPMC7010779 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Training deep quantum neural networks.

Beer Kerstin K Bondarenko Dmytro D Farrelly Terry T Osborne Tobias J TJ Salzmann Robert R Scheiermann Daniel D Wolf Ramona R

Nature communications 20200210 1

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 ...[more]

PMID: 32041956

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