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

International Joint Conference on Neural Networks (IJCNN)

An autonomous competitive learning algorithm using quantum hamming neural networks

Mohammed Zidan, Alaa Sagheer, NASSER METWALLY

Published Jul 1, 2015• 16 citations
Publisher

Abstract

Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network (QHNN), is capable to recognize incomplete patterns, as well as, increase the probability of recognizing patterns on the account of undesired patterns. Moreover, these undesired patterns could be used as new patterns for training the algorithm in subsequent steps. The proposed algorithm is testified via a case study and a classification experiment, where promising results, reaches to 100%, are given and compared favorably with other reported quantum competitive algorithms.

DOI
10.1109/ijcnn.2015.7280719

Related papers

Quantum Classification Algorithm Based on Competitive Learning Neural Network and Entanglement Measure

Mohammed Zidan, Abdel‐Haleem Abdel‐Aty, Mahmoud El-shafei +4 more

In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.

Applied Sciences•Mar 27, 2019•79 citations

Quantum Competition Network Model Based On Quantum Entanglement

Yanhua Zhong, Chang-qing Yuan

This paper proposes a quantum competition neural network model compared to its classical counterpart from the relative parts of the complex system localizing operation without changing the perspective of entanglement measure. It shows that the pseudo-state is an inevitable part of the quantum competitive model. After the initialization of the quantum neural network; quantum competitive network is capable of associative memory through local area of operations because of the existence of these pseudo-states. Furthermore, Competitive algorithms of quantum theory are given, and finally an example of pattern recognition for simulation. Simulation results show that a quantum competitive learning algorithm in the learning rate and convergence rate is far better than the basic competitive artificial neural network.

Journal of Computers•Sep 1, 2012•9 citations

Quantum Neural Computation Technology

Guangjun Xie

Through the comparison between the quantum computation and the neural computation,a new technology called quantum neural computation is presented.A generalized quantum Hebb learning algorithm,a quantum neural network model based on the double-slit interference experiment,and a quantum Hamming competitive learning algorithm are discussed;at the same time some essential characters of the quantum neural computation are also revealed.

Journal of Jishou University•Jan 1, 2005•0 citations

Quantum Neural Networks: Self-Organized Learning for Enhanced Pattern Recognition

Jincheng Zhang

This paper explores the potential of quantum neural networks (QNNs) leveraging self-organization to achieve superior performance in pattern recognition and classification tasks. The core concept involves constructing a QNN utilizing quantum phenomena like decoherence and entanglement, followed by employing self-organizing learning algorithms to dynamically adjust the network's connection weights and topology. We posit that this combination can significantly enhance learning efficiency and accuracy compared to classical neural networks. The theoretical framework presented outlines a novel approach, and while experimental validation is not included, the proposed architecture and learning process provide a foundational understanding for future research and development in this emerging field. Key elements include the mathematical formulation of the QNN's dynamics, the self-organization learning algorithm, and the evaluation metrics used to assess performance. The primary goal is to demonstrate a theoretical pathway toward a more efficient and robust QNN through self-adaptive learning.

•Aug 31, 2026•0 citations

基于量子神经网络的非线性学习

Jincheng Zhang

Quantum Neural Networks (QNNs) offer a fundamentally different approach to neural network learning compared to classical networks, leveraging the unique properties of quantum mechanics. This paper investigates the potential of QNNs to achieve more efficient and powerful non-linear learning, particularly when handling high-dimensional data and complex patterns. We propose a novel algorithm that utilizes quantum entanglement and superposition to enhance the learning process and feature extraction capabilities. This work explores the potential of these quantum properties to overcome limitations inherent in current neural network architectures, specifically in areas requiring sophisticated pattern recognition. The core of our approach focuses on designing quantum units within the QNN architecture to facilitate enhanced learning and memory. The paper details the proposed algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its effectiveness in a simulated environment. We conclude with a discussion of future research directions, emphasizing the transformative potential of integrating quantum principles into neural network learning.

•Sep 7, 2026•0 citations

Superposition Based Learning Algorithm

Adenilton José da Silva, Teresa B. Ludermir, Wilson Rosa de Oliveira

By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known examples are the Shor's factoring algorithm and the Grover's search algorithm. This paper investigates the possibility of employing Quantum Computing techniques to the design of learning algorithms for neural networks tasks such as pattern recognition. We propose a quantum learning algorithm for neural networks where all patterns of the training set are presented concurrently in superposition. In the process we propose a novel model of a quantum weightless neural node. The algorithm is a combination of a quantum search algorithm, a probabilistic quantum memory and a quantum neural network.

Brazilian Symposium on Neural Networks (SBRN)•Oct 1, 2010•10 citations
Quantum neural network is a fledging research domain based on the merge of classical neural network and quantum computing. In this paper, we add the quantum effect to the classical hamming neural network algorithm in order to employ the advantages of quantum information to yield, finally, a novel quantum competitive learning algorithm. The proposed algorithm, called quantum hamming neural network (QHNN), is capable to recognize incomplete patterns, as well as, increase the probability of recognizing patterns on the account of undesired patterns. Moreover, these undesired patterns could be used as new patterns for training the algorithm in subsequent steps. The proposed algorithm is testified via a case study and a classification experiment, where promising results, reaches to 100%, are given and compared favorably with other reported quantum competitive algorithms.
In this paper, we develop a novel classification algorithm that is based on the integration between competitive learning and the computational power of quantum computing. The proposed algorithm classifies an input into one of two binary classes even if the input pattern is incomplete. We use the entanglement measure after applying unitary operators to conduct the competition between neurons in order to find the winning class based on wining-take-all. The novelty of the proposed algorithm is shown in its application to the quantum computer. Our idea is validated via classifying the state of Reactor Coolant Pump of a Risky Nuclear Power Plant and compared with other quantum-based competitive neural networks model.
This paper proposes a quantum competition neural network model compared to its classical counterpart from the relative parts of the complex system localizing operation without changing the perspective of entanglement measure. It shows that the pseudo-state is an inevitable part of the quantum competitive model. After the initialization of the quantum neural network; quantum competitive network is capable of associative memory through local area of operations because of the existence of these pseudo-states. Furthermore, Competitive algorithms of quantum theory are given, and finally an example of pattern recognition for simulation. Simulation results show that a quantum competitive learning algorithm in the learning rate and convergence rate is far better than the basic competitive artificial neural network.
This paper explores the potential of quantum neural networks (QNNs) leveraging self-organization to achieve superior performance in pattern recognition and classification tasks. The core concept involves constructing a QNN utilizing quantum phenomena like decoherence and entanglement, followed by employing self-organizing learning algorithms to dynamically adjust the network's connection weights and topology. We posit that this combination can significantly enhance learning efficiency and accuracy compared to classical neural networks. The theoretical framework presented outlines a novel approach, and while experimental validation is not included, the proposed architecture and learning process provide a foundational understanding for future research and development in this emerging field. Key elements include the mathematical formulation of the QNN's dynamics, the self-organization learning algorithm, and the evaluation metrics used to assess performance. The primary goal is to demonstrate a theoretical pathway toward a more efficient and robust QNN through self-adaptive learning.
Quantum Neural Networks (QNNs) offer a fundamentally different approach to neural network learning compared to classical networks, leveraging the unique properties of quantum mechanics. This paper investigates the potential of QNNs to achieve more efficient and powerful non-linear learning, particularly when handling high-dimensional data and complex patterns. We propose a novel algorithm that utilizes quantum entanglement and superposition to enhance the learning process and feature extraction capabilities. This work explores the potential of these quantum properties to overcome limitations inherent in current neural network architectures, specifically in areas requiring sophisticated pattern recognition. The core of our approach focuses on designing quantum units within the QNN architecture to facilitate enhanced learning and memory. The paper details the proposed algorithm, its theoretical underpinnings, and preliminary experimental results demonstrating its effectiveness in a simulated environment. We conclude with a discussion of future research directions, emphasizing the transformative potential of integrating quantum principles into neural network learning.
By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known examples are the Shor's factoring algorithm and the Grover's search algorithm. This paper investigates the possibility of employing Quantum Computing techniques to the design of learning algorithms for neural networks tasks such as pattern recognition. We propose a quantum learning algorithm for neural networks where all patterns of the training set are presented concurrently in superposition. In the process we propose a novel model of a quantum weightless neural node. The algorithm is a combination of a quantum search algorithm, a probabilistic quantum memory and a quantum neural network.