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.
2 More- 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)
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.