Advanced quantum image representation and compression using a DCT-EFRQI approach

Author:

Haque Md Ershadul,Paul Manoranjan,Ulhaq Anwaar,Debnath Tanmoy

Abstract

AbstractIn recent years, quantum image computing draws a lot of attention due to storing and processing image data faster compared to classical computers. A number of approaches have been proposed to represent the quantum image inside a quantum computer. Representing and compressing medium and big-size images inside the quantum computer is still challenging. To address this issue, we have proposed a block-wise DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) approach to represent and compress the gray-scale image efficiently to save computational time and reduce the quantum bits (qubits) for the state preparation. In this work, we have demonstrated the capability of block-wise DCT and DWT transformation inside the quantum domain to investigate their relative performances. The Quirk simulation tool is used to design the corresponding quantum image circuit. In the proposed DCT-EFRQI approach, a total of 17 qubits are used to represent the coefficients, the connection between coefficients and state (i.e., auxiliary), and their position for representing and compressing grayscale images inside a quantum computer. Among those, 8 qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position including one auxiliary qubit. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of rate-distortion performance.

Funder

RTP

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference34 articles.

1. Khan, R. A. An improved flexible representation of quantum images. Quant. Inf. Process. 1, 1–19 (2019).

2. Jacobs, I. Fine particles, thin films and exchange anisotropy. Magnetism 1, 271–350 (1963).

3. Venegas-Andraca, S. E. & Bose, S. Storing, processing, and retrieving an image using quantum mechanics. Quant. Inf. Comput. 5105, 137–147 (2003).

4. Ladd, T. D. et al. Quantum computers. Nature 464, 45–53 (2010).

5. Mandra, S., Guerreschi, G. G. & Aspuru-Guzik, A. Faster than classical quantum algorithm for dense formulas of exact satisfiability and occupation problems. N. J. Phys. 18, 073003 (2016).

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3