Abstract
This study surveys the current status of Quantum Deep Learning Neural Networks. Exciting breakthroughs may soon bring real quantum neural networks, specifically deep learning neural networks, to reality. Three main obstacles have been limiting quantum growth in the deep learning area, and this study has found that new discoveries have changed these obstacles. The first obstacle was the lack of a real quantum computers to experiment with, not simulators. Several companies have significantly increased their inventory of quantum computers in the last year, including IBM. The second obstacle was the impossibility of training quantum networks, but a new algorithm solves this problem. The third obstacle was that neural networks have nonlinear functions, but that has been solved with a new quantum perceptron. This study explains the historical background briefly for context and understanding, then describes these three major accomplishments that will likely lead to real quantum deep learning neural networks.
Thanks to the IBM Faculty Award that made this research possible.
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Kamruzzaman, A., Alhwaiti, Y., Leider, A., Tappert, C.C. (2020). Quantum Deep Learning Neural Networks. In: Arai, K., Bhatia, R. (eds) Advances in Information and Communication. FICC 2019. Lecture Notes in Networks and Systems, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-030-12385-7_24
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DOI: https://doi.org/10.1007/978-3-030-12385-7_24
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