ABSTRACT
Techniques for exploiting visual information play an important and topical role. Image processing is an area of Artificial Intelligence, dealing with how to represent, reconstruct, classify, recognition and analysis of images using computers. The idea behind this study is to follow how the quantum properties of superposition and entanglement are used by the Bit-plane Representation of Quantum Images BRQI model, grayscale image, to map and store the image as qubits. For this, we used the IBM Quantum Experience platform that provides the opportunity to implement and run programs with quantum applications.
- Phuc Le, Abdullah M. Iliyasu, Fangyan Dong, and Kaoru Hirota. 2011. A flexible representation and invertible transformations for images on quantum computers. in New Advances in Intelligent Signal Processing, vol. 372. Springer, pp. 179-202. [Online]. Available: https://link.springer.com/chapter/10.1007%2F978-3-642-11739-8_9.Google Scholar
- Hai-Sheng Li, Qingxin Zhu, RiGui Zhou, Lang Song, and Xing-jiang Yang. 2014. Multi-dimensional color image storage and retrieval for a normal arbitrary quantum superposition state. Quantum Inf. Process., vol. 13, no. 4, pp. 991-1011.Google ScholarDigital Library
- Hai-Sheng Li, Zhu Qingxin, Song Lan, Chen-Yi Shen, RiGui Zhou, and Jia Mo. 2013. Image storage, retrieval, compression and segmentation in a quantum system. Quantum Inf. Process., vol. 12, no. 6, pp. 2269-2290.Google ScholarDigital Library
- Kai Lu, Yi Zhang, Yinghui Gao, and Kai Xu. 2013. A novel quantum representation for log-polar images. Quantum Inf. Process., vol. 12, no. 9, pp. 3103-3126.Google ScholarDigital Library
- Yi Zhang, Kai Lu, Yinghui Gao, and M. Wang. 2013. NEQR: A novel enhanced quantum representation of digital images. Quantum Inf. Process., vol. 12, no. 8, pp. 2833-2860.Google ScholarDigital Library
- Bo Sun, Abdullah M. Iliyasu, Fei Yan, Fangyan Dong, and Kaoru Hirota. 2013. An RGB multichannel representation for images on quantum computers. J. Adv. Comput. Intell. Inform., vol. 17, no. 3, pp. 404-417.Google ScholarCross Ref
- Hai-Sheng Li, Xiao Chen, Haiying Xia, Yan Liang, and Zoushan Zhou. 2018. A quantum image representation based on bitplanes. IEEE Access, vol. 6, pp. 62396-62404.Google ScholarCross Ref
- https://www.ibm.com/quantum-computing/. Retrieved. 2021.Google Scholar
- https://qiskit.org/textbook/ch-applications/image-processing-frqi-neqr.html. retrieved.2021.Google Scholar
- Jie Su, Xuchao Guo, Chengqi Liu, Shuhan Lu and Lin Li. 2021. An improved novel quantum image representation and its experimental test on IBM quantum experience. Scientific Report, Nature.Google Scholar
- Robert Brayton, Gary Hachtel, Carol McMullen, Alberto Sangiovanni-Vincentelli. 1984. Log Minimization Algorithms VLSI Synch. Kluwer Academic Publishers, Dordrecht.Google Scholar
Recommendations
Quantum image scaling using nearest neighbor interpolation
Although image scaling algorithms in classical image processing have been extensively studied and widely used as basic image transformation methods, the quantum versions do not exist. Therefore, this paper proposes quantum algorithms and circuits to ...
A quantum watermarking scheme using simple and small-scale quantum circuits
A new quantum gray-scale image watermarking scheme by using simple and small-scale quantum circuits is proposed. The NEQR representation for quantum images is used. The image sizes for carrier and watermark are assumed to be $$2n \times 2n$$2n 2n and $$...
A survey of quantum image representations
Quantum image processing (QIMP) is devoted to utilizing the quantum computing technologies to capture, manipulate, and recover quantum images in different formats and for different purposes. Logically, percolating this requires that representations to ...
Comments