Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Quantum Information Processing

Analysis and improvement of the quantum image matching

Yijie Dang, Nan Jiang, Hao Hu, Wenyin Zhang

Published Sep 27, 2017• 18 citations
Publisher
DOI
10.1007/s11128-017-1723-7

Related papers

Quantum image matching

Nan Jiang, Yijie Dang, Jian Wang

arXiv:1605.09729•Quantum Information Processing•Jun 10, 2016•89 citations

Analysis and improvement of the quantum Arnold image scrambling

Nan Jiang, Luo Wang

Quantum Information Processing•May 10, 2014•82 citations

Quantum Image Reversible Watermarking Scheme using a Combination of the most Significant Qubit and the Least Significant Qubit

Gaofeng Luo, Ling Shi, Tongcheng Huang +1 more

Quantum image watermarking technology is influenced by embedding algorithm, which permanently distorts the embedded quantum carrier image.To protect and authenticate quantum images without damaging the carrier images, this study proposed a quantum image reversible watermarking algorithm combining the most significant qubit (MSQb) and the least significant qubit (LSQb).First, a binary watermark image, a grayscale carrier image, and an empty key image were represented using the novel enhanced quantum representation of digital images.Second, the encrypted qubit of the binary watermark image was matched with the MSQb of the carrier image.If they were equal, then the high qubit of the quantum key image was flipped.Otherwise, the encrypted qubit of the binary watermark image was matched with the XOR value of the lowest two qubits of the carrier image.The LSQb of the carrier image was modified according to the matching result.Finally, the quantum watermarked image was obtained.Simulation experiments and theoretical analysis verified the feasibility and watermark performance of the scheme.Results demonstrate that the scheme achieves correct extraction of quantum image watermark and lossless restoration of original image.The visual quality PSNR of the carrier image embedded with watermark through the scheme is improved by approximately 4% compared with that through the traditional LSQb method.The study provides a reference for studying and implementing reversible image watermarking technology in the era of quantum computer, which can be applied to harsh copyright protection and authentication of the visual quality of the original carrier image.

Journal of Engineering Science and Technology Review•Jan 1, 2020•1 citation

Quantum Image Processing and Its Applications

J. Jennifer Ranjani

Springer eBooks•Jan 1, 2019•0 citations

基于量子纠缠的自适应特征匹配

Jincheng Zhang

This paper introduces a novel approach to feature matching leveraging quantum entanglement. Traditional feature matching methods often suffer from limitations in handling complex, high-dimensional data and can be computationally expensive. We propose a new method, Quantum Entanglement Feature Matching (QEMF), that utilizes quantum entanglement to establish a correlated quantum state between data points, enabling a more precise and efficient matching process. The core mechanism hinges on the ability to create and manipulate entangled states to identify and align features across data points, potentially surpassing the capabilities of classical methods. We demonstrate the effectiveness of QEMF through rigorous analysis and simulations, showcasing significant improvements in accuracy and speed compared to existing techniques. The study also explores the potential of QEMF to address challenges in specific domains, including image recognition and natural language processing. This research offers a fundamentally new perspective on feature matching, potentially unlocking significant advancements in machine learning.

•Sep 4, 2026•0 citations

Recognition of image data in quantum computer

Tetsuo Hattori, Katsunori Takeda, Toshinori Yamasaki

In order to image recognition using a register-type quantum computer consisting of electronic spins, this paper presents a unitary transformation for quantum state image representation of input image, and another unitary transformation for matching (or similarity computation) between input image and reference image data. Moreover, it shows some improved image recognition algorithms by quantum computer, based on Gram-Schmidt's orthogonalization.

Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE•Jul 13, 2000•1 citation
Quantum image watermarking technology is influenced by embedding algorithm, which permanently distorts the embedded quantum carrier image.To protect and authenticate quantum images without damaging the carrier images, this study proposed a quantum image reversible watermarking algorithm combining the most significant qubit (MSQb) and the least significant qubit (LSQb).First, a binary watermark image, a grayscale carrier image, and an empty key image were represented using the novel enhanced quantum representation of digital images.Second, the encrypted qubit of the binary watermark image was matched with the MSQb of the carrier image.If they were equal, then the high qubit of the quantum key image was flipped.Otherwise, the encrypted qubit of the binary watermark image was matched with the XOR value of the lowest two qubits of the carrier image.The LSQb of the carrier image was modified according to the matching result.Finally, the quantum watermarked image was obtained.Simulation experiments and theoretical analysis verified the feasibility and watermark performance of the scheme.Results demonstrate that the scheme achieves correct extraction of quantum image watermark and lossless restoration of original image.The visual quality PSNR of the carrier image embedded with watermark through the scheme is improved by approximately 4% compared with that through the traditional LSQb method.The study provides a reference for studying and implementing reversible image watermarking technology in the era of quantum computer, which can be applied to harsh copyright protection and authentication of the visual quality of the original carrier image.
This paper introduces a novel approach to feature matching leveraging quantum entanglement. Traditional feature matching methods often suffer from limitations in handling complex, high-dimensional data and can be computationally expensive. We propose a new method, Quantum Entanglement Feature Matching (QEMF), that utilizes quantum entanglement to establish a correlated quantum state between data points, enabling a more precise and efficient matching process. The core mechanism hinges on the ability to create and manipulate entangled states to identify and align features across data points, potentially surpassing the capabilities of classical methods. We demonstrate the effectiveness of QEMF through rigorous analysis and simulations, showcasing significant improvements in accuracy and speed compared to existing techniques. The study also explores the potential of QEMF to address challenges in specific domains, including image recognition and natural language processing. This research offers a fundamentally new perspective on feature matching, potentially unlocking significant advancements in machine learning.