• Open Access

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Xi-Wei Yao, Hengyan Wang, Zeyang Liao, Ming-Cheng Chen, Jian Pan, Jun Li, Kechao Zhang, Xingcheng Lin, Zhehui Wang, Zhihuang Luo, Wenqiang Zheng, Jianzhong Li, Meisheng Zhao, Xinhua Peng, and Dieter Suter
Phys. Rev. X 7, 031041 – Published 11 September 2017

Abstract

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission, and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.

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  • Received 26 April 2017

DOI:https://doi.org/10.1103/PhysRevX.7.031041

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Xi-Wei Yao1,4,5,*, Hengyan Wang2, Zeyang Liao3, Ming-Cheng Chen6, Jian Pan2, Jun Li7, Kechao Zhang8, Xingcheng Lin9, Zhehui Wang10, Zhihuang Luo7, Wenqiang Zheng11, Jianzhong Li12, Meisheng Zhao13, Xinhua Peng2,14,†, and Dieter Suter15,‡

  • 1Department of Electronic Science, College of Physical Science and Technology, Xiamen University, Xiamen, Fujian 361005, China
  • 2CAS Key Laboratory of Microscale Magnetic Resonance and Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 3Institute for Quantum Science and Engineering (IQSE) and Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843-4242, USA
  • 4Dahonggou Haydite Mine, Urumqi, Xinjiang 831499, China
  • 5College of Physical Science and Technology, Yili University, Yining, Xinjiang 835000, China
  • 6Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 7Beijing Computational Science Research Center, Beijing 100193, China
  • 8Institute of Theoretical Physics, Chinese Academy of Sciences, Beijing 100190, China
  • 9Department of Physics & Astronomy and Center for Theoretical Biological Physics, Rice University, Houston, Texas 77005, USA
  • 10School of Mathematical Sciences, Peking University, Beijing 100871, China
  • 11Center for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China
  • 12College of mathematics and statistics, Hanshan Normal University, Chaozhou, Guangdong 521041, China
  • 13Shandong Institute of Quantum Science and Technology, Co., Ltd., Jinan, Shandong 250101, China
  • 14Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China
  • 15Fakultät Physik, Technische Universität Dortmund, D-44221 Dortmund, Germany

  • *yau@xmu.edu.cn
  • xhpeng@ustc.edu.cn
  • dieter.suter@tu-dortmund.de

Popular Summary

Visual information processing and analysis has a diverse range of applications such as biomedical engineering, artificial intelligence, automatic piloting, and satellite remote sensing. The ever-increasing amount of image data has become enormous, the analysis of which requires extraordinary amounts of computational power. Quantum computing promises to overcome the limits of traditional digital computers by leveraging bizarre quantum effects such as the ability of particles to exist in multiple states simultaneously. In this paper, we show how these advantages could be used to develop highly efficient image processing algorithms.

Our approach to image processing encodes the image information in the probability amplitudes of individual basis states, each of which corresponds to one pixel of the image. Using this quantum image representation, we demonstrate a basic framework of quantum image processing and propose a novel quantum algorithm for image edge detection that is exponentially faster than the classical algorithms, as well as the first experimental demonstrations of this algorithm. Remarkably, the new quantum algorithm requires only one single-qubit gate, independent of the size of the picture. Our results clearly show the potential of quantum computation for image processing.

Because of the widespread importance of visual information processing and its tremendous consumption of computational resources, quantum speedup is an extremely attractive solution to the challenges of big data. We expect that our findings will stimulate future studies of quantum algorithms for visual information processing.

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Vol. 7, Iss. 3 — July - September 2017

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