Quantum Circuits for Binary Convolution - Yetis, Hasan; Karakose, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2020)
Deep learning is used in different research topics and draws attention with its successful results. Today, the resource need of deep learning, which requires much data and resources, is tried to be met by technologies such as parallel processors and graphic processors. In this study, the convolution process, which is one of the main causes of resource need in deep learning, is performed with quantum circuits. Thanks to the convolution process run on quantum computers, whose computation speeds are many times higher than supercomputers, the training step of deep learning can be performed much faster. In this way, the need for performance, which is one of the biggest obstacles in front of deep learning and forces researchers to work on pre-trained networks, will be met. With running deep learning networks on quantum computers, it is predicted by the scientific world that a new era for deep learning will start. Although we propose quantum circuits for the convolution process with 2, 3, and 4 window sizes, it can be used for only one iteration today. However, with obtaining structures such as quantum ram and developing sequential processing architecture for quantum computers, the proposed circuits can be used for a complete convolution process. © 2020 IEEE.
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Keywords: convolution; deep learning; quantum circuits; quantum computers; quantum computing