Abstract
AbstractNeural 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.
Funder
Deutsche Forschungsgemeinschaft
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry
Reference46 articles.
1. Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).
2. Nielsen, M. A. Neural Networks and Deep Learning (Determination Press, 2015).
3. Jordan, M. I. & Mitchell, T. M. Machine learning: trends, perspectives, and prospects. Science 349, 255–260 (2015).
4. Bishop, C. Pattern Recognition and Machine Learning. Information Science and Statistics (Springer-Verlag, 2006).
5. Prati, E., Rotta, D., Sebastiano, F. & Charbon, E. From the quantum Moore’s law toward silicon based universal quantum computing. in 2017 IEEE ICRC (2017).
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