Open Access

ARTICLE

An Image Localization System Based on Single Photon

by Yanyi Wu¹, Xiaoyu Li², Qinsheng Zhu^1,*, Xiaolei Liu², Hao Wu¹, Shan Yang³

1 School of Physics, University of Electronic Science and Technology of China, Chengdu, 610054, China
2 School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, China
3 Department of Chemistry, Physics and Atmospheric Sciences, Jackson State University, Jackson, USA

* Corresponding Author: Qinsheng Zhu. Email:

Computers, Materials & Continua 2022, 73(3), 6139-6149. https://doi.org/10.32604/cmc.2022.032086

Received 06 May 2022; Accepted 09 June 2022; Issue published 28 July 2022

Abstract

As an essential part of artificial intelligence, many works focus on image processing which is the branch of computer vision. Nevertheless, image localization faces complex challenges in image processing with image data increases. At the same time, quantum computing has the unique advantages of improving computing power and reducing energy consumption. So, combining the advantage of quantum computing is necessary for studying the quantum image localization algorithms. At present, many quantum image localization algorithms have been proposed, and their efficiency is theoretically higher than the corresponding classical algorithms. But, in quantum computing experiments, quantum gates in quantum computing hardware need to work at very low temperatures, which brings great challenges to experiments. This paper proposes a single-photon-based quantum image localization algorithm based on the fundamental theory of single-photon image classification. This scheme realizes the operation of the mixed national institute of standards and technology database (MNIST) quantum image localization by a learned transformation for non-noise condition, noisy condition, and environmental attack condition, respectively. Compared with the regular use of entanglement between multi-qubits and low-temperature noise reduction conditions for image localization, the advantage of this method is that it does not deliberately require low temperature and entanglement resources, and it improves the lower bound of the localization success rate. This method paves a way to study quantum computer vision.

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Keywords

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