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Journal of Computers

Quantum Competition Network Model Based On Quantum Entanglement

Yanhua Zhong, Chang-qing Yuan

Published Sep 1, 2012• 9 citations
Publisher

Abstract

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.

DOI
10.4304/jcp.7.9.2312-2317

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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.
A novel neural networks model with quantum weights is present.Firstly, based on the information processing modes of biology neuron and quantum computing theory, a quantum neuron model is presented, which is composed of weighting, aggregating, activating, and inspiriting.Secondly the quantum neural networks model based on quantum neuron is constructed in which the input and the output are real vectors; the linked weight and the activation value are qubits.On the basic of the gradient descent algorithm, a learning algorithm of this model is proposed.It is shown that this algorithm is super-linearly convergent under certain conditions and can increase the probability of getting the global optimal solution.Finally, the availability of the model is illustrated in both convergence speed and convergence rate by two application examples of pattern recognition and function approximation.
The subject of the paper is Quantum Neural Network. On one hand, introducing quantum theory into the structure or training process of Classical Neural Network with regard to improving structure and capacity of Classical Neural Network, enhancing learning and generalization ability of it. On the other hand, establishing a new topological structure and training algorithm of Quantum Neural Network by the means of quoting the thought, concept and principles of quantum theory directly. In the paper, we summarize the basic model and learning algorithm of quantum neuron, propose the model and learning algorithm of Quantum Adaptive Resonance Theory Neural Network through introducing quantum computing into adaptive resonance theory, apply which in pattern recognition as well. In summary, Quantum Adaptive Resonance Theory Neural Network have an advantage over Classical Adaptive Resonance Theory Neural Network in the positive rate of clustering.
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.