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.
- Publication:
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Nature Communications
- Pub Date:
- February 2020
- DOI:
- 10.1038/s41467-020-14454-2
- arXiv:
- arXiv:1902.10445
- Bibcode:
- 2020NatCo..11..808B
- Keywords:
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- Quantum Physics;
- Computer Science - Computer Science and Game Theory;
- Computer Science - Machine Learning;
- Physics - Computational Physics
- E-Print:
- Nat. Commun. 11, 808 (2020)