Binary Pooling Circuits for Quantum Computing
Hasan Yetış, Mehmet Karaköse · 2021 International Conference on Decision Aid Sciences and Application (DASA) · 2021
Despite the successful results they have provided, the need of high computational power is the biggest obstacle in front of deep learning. In this study, the pooling operation that is used many times in a deep learning network is carried out with quantum circuits. In order to make able testing of the proposed circuits on today's quantum computers, binary inputs are used. In today's technology, it is not able to run whole deep learning process on reliable quantum computers. But, when it is possible to perform all deep learning steps much faster on quantum computers, it will eliminate the need to use pre-trained networks. With training the deep learning networks from scratch in much shorter time, a new era will start for deep learning. In this study, quantum binary pooling circuits are proposed for this purpose. Quantum circuits are proposed for 3, 4, 5, and 9-dimensional windows. In today's quantum computers that do not work iteratively, the proposed circuit only works for 1 step. In order to apply pooling to the whole image, the circuit must be looped. With the development of the relevant quantum computer architecture, the method will become applicable to the whole image.