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

Balkan Conference in Informatics

Applying a quantum amplification over a system for image feature matching and image recommendation

Desislav Andreev, Milena Kirilova Lazarova

Published Sep 5, 2019• 1 citation
Publisher

Abstract

A novel approach for improving the performance of a image feature matching and recommendation system is proposed that is a major continuation of the previous work in [3]. An advanced clustering method is suggested in order to deal with the binary image feature descriptors: a quantum variant of the k-majority algorithm over ORB descriptors. Jaccard-Needham dissimilarity measure is used as a distance measure step of the algorithm. Finally, the Grover's algorithm is used, providing the opportunity for a specific feature search in the database. The paper also provides the main steps in constructing a similar fast search system. The transformation from a classical to a quantum representation algorithm is described. Such approach can be applied in other applications. Both the computational complexity and the verification correctness are also indicated in the paper.

DOI
10.1145/3351556.3351564

Related papers

Grover search revisited: Application to image pattern matching

Hiroyuki Tezuka, Kouhei Nakaji, Takahiko Satoh +1 more

The landmark Grover algorithm for amplitude amplification serves as an essential subroutine in various types of quantum algorithms, with guaranteed quantum speedup in query complexity. However, there has been no proposal to realize the original motivating application of the algorithm, i.e., the database search or more broadly the pattern matching in a practical setting, mainly due to the technical difficulty in efficiently implementing the data loading and amplitude amplification processes. In this paper, we propose a quantum algorithm that approximately executes the entire Grover database search or pattern matching algorithm. The key idea is to use the recently proposed approximate amplitude encoding method on a shallow quantum circuit, together with the easily implementable inversion-test operation for realizing the projected quantum state having similarity to the query data, followed by the amplitude amplification operation that is independent to the target data index. We provide a thorough demonstration of the algorithm in the problem of image pattern matching.

arXiv:2108.10854•Physical Review A•Mar 24, 2022•35 citations

Quantum Computing Approaches for High-Speed Visual Search

Haewon Byeon, Annabathula Phani Sheetal, Musaddak Maher Abdul Zahra +3 more

This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need$100-150 ~\text{ms}$. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.

International Conference on Networks and Cryptology (NETCRYPT)•May 29, 2025•1 citation

Recommendation systems with the quantum k–NN and Grover algorithms for data processing

Marek Sawerwain, Marek Wróblewski

Abstract In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in an unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows the main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps in the recommendation system is also indicated. The verification of the correctness of the proposed system is analysed as well, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.

arXiv:1812.05095•International Journal of Applied Mathematics and Computer Science•Mar 1, 2019•12 citations

A Hybrid Quantum Image-Matching Algorithm

Guo-Qiang Shu, Zheng Shan, Shiqin Di +2 more

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.

Entropy•Dec 13, 2022•3 citations

Intelligent recommender system based on quantum clustering and matrix completion

Oumayma Ouedrhiri, Oumayma Banouar, Salah El Hadaj +1 more

Abstract Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time‐consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource‐consuming step, especially when working with large datasets. In this article, we propose a new better‐performing recommender system that operates as a combination of an adapted quantum K‐means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K‐means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.

Concurrency and Computation Practice and Experience•Mar 15, 2022•17 citations

Quantum Techniques for Image Processing

Arti Ranjan, Ashish K. S. Arya, M. Ravinder

Quantum computing is revolutionizing every field of knowledge. With advent of affordable quantum computing almost possible the importance and relevance of quantum computing for all the recent research trends cannot be overemphasized. We have already witnessed quantum analogues of most celebrated classical algorithms starting from Shor’s algorithm for integer factorization, Grover’s search algorithms for unstructured database. Quantum Image Processing is an emerging field borne by applying quantum computing techniques to the Image Processing problems. This paper attempts to summarize and review the state of development in the field of Quantum Image Processing. All major quantum computing platforms have been presented, At the end a discussion on the current trends and future possibilities is presented.

International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)•Dec 18, 2020•11 citations
A novel approach for improving the performance of a image feature matching and recommendation system is proposed that is a major continuation of the previous work in [3]. An advanced clustering method is suggested in order to deal with the binary image feature descriptors: a quantum variant of the k-majority algorithm over ORB descriptors. Jaccard-Needham dissimilarity measure is used as a distance measure step of the algorithm. Finally, the Grover's algorithm is used, providing the opportunity for a specific feature search in the database. The paper also provides the main steps in constructing a similar fast search system. The transformation from a classical to a quantum representation algorithm is described. Such approach can be applied in other applications. Both the computational complexity and the verification correctness are also indicated in the paper.
The landmark Grover algorithm for amplitude amplification serves as an essential subroutine in various types of quantum algorithms, with guaranteed quantum speedup in query complexity. However, there has been no proposal to realize the original motivating application of the algorithm, i.e., the database search or more broadly the pattern matching in a practical setting, mainly due to the technical difficulty in efficiently implementing the data loading and amplitude amplification processes. In this paper, we propose a quantum algorithm that approximately executes the entire Grover database search or pattern matching algorithm. The key idea is to use the recently proposed approximate amplitude encoding method on a shallow quantum circuit, together with the easily implementable inversion-test operation for realizing the projected quantum state having similarity to the query data, followed by the amplitude amplification operation that is independent to the target data index. We provide a thorough demonstration of the algorithm in the problem of image pattern matching.
This work introduces a rapid visual search method for biometric recognition, medical imaging, security monitoring, and multimedia retrieval. Traditional visual search methods involve laborious, unsuccessful pixel-by-pixel comparisons and generated feature descriptors for large datasets. The revolutionary new option of quantum computing uses superposition, entanglement, and parallelism to boost feature discovery, computing similarities, and fine-tuning findings. This paper suggests a fast way to search for images using quantum computing, which uses the Quantum Fourier Transform (QFT) to identify features and quantum similarity measurements to compare images. The suggested solution greatly reduces processing time and improves retrieval accuracy. Performance tests demonstrate quantum computing outperforms classical approaches. Quantum computing has a 25 ms working latency, while standard systems need100−150 ms100-150 ~\text{ms}100−150 ms. With 95% accuracy and scalability, quantum-based search outperforms traditional algorithms. The quantum approach uses 90 joules, while normal systems use 230-280. So, it takes less energy. Quantum computing is helpful and extensible because it handles noise better and uses less memory. Even though quantum computing is difficult to set up, its benefits in computation, parallelism, and real-world usability demonstrate that it could revolutionize visual search technology. Quantum technologies will make large-scale photo retrieval faster and more precise as quantum computing improves.
Abstract In this article, we discuss the implementation of a quantum recommendation system that uses a quantum variant of the k-nearest neighbours algorithm and the Grover algorithm to search for a specific element in an unstructured database. In addition to the presentation of the recommendation system as an algorithm, the article also shows the main steps in construction of a suitable quantum circuit for realisation of a given recommendation system. The computational complexity of individual calculation steps in the recommendation system is also indicated. The verification of the correctness of the proposed system is analysed as well, indicating an algebraic equation describing the probability of success of the recommendation. The article also shows numerical examples presenting the behaviour of the recommendation system for two selected cases.
Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.
Abstract Faster than classical algorithms, quantum algorithms benefit from the superposition property of quantum information to offer significant speedup to complex algorithms. Therefore, quantum computing can be used to help machine learning algorithms by boosting their performance and accelerate the processing of time‐consuming ones. Clustering algorithms are very complex unsupervised learning algorithms. Indeed, the similarity calculation (distance) between input vectors is a resource‐consuming step, especially when working with large datasets. In this article, we propose a new better‐performing recommender system that operates as a combination of an adapted quantum K‐means algorithm and the singular value decomposition (SVT) algorithm. We integrate the developed quantum clustering algorithm to a prediction process of the proposed recommender system using matrix completion. To the best of our knowledge, no system with such details was proposed in the literature. The system was applied on the MovieLens dataset without a dimensionality reduction step and evaluated according to measures of information retrieval systems. The results of the quantum K‐means algorithm show that the quantum version leads to a logarithmic reduction of the time complexity compared to the classical algorithm. The proposed system has proved to be better than the previous tested ones in terms of precision and recall.
Quantum computing is revolutionizing every field of knowledge. With advent of affordable quantum computing almost possible the importance and relevance of quantum computing for all the recent research trends cannot be overemphasized. We have already witnessed quantum analogues of most celebrated classical algorithms starting from Shor’s algorithm for integer factorization, Grover’s search algorithms for unstructured database. Quantum Image Processing is an emerging field borne by applying quantum computing techniques to the Image Processing problems. This paper attempts to summarize and review the state of development in the field of Quantum Image Processing. All major quantum computing platforms have been presented, At the end a discussion on the current trends and future possibilities is presented.