Electronic and Mechanical Engineering and Information Technology

Research of model of Quantum Learning Vector Quantization Neural Network

Xuan Hou

Published Aug 1, 2011• 14 citations

Abstract

This paper has been studied in Quantum Learning Vector Quantization Neural Network. The concept of competition and model of competitive network has been studied. It analyses model of Quantum Learning Vector Quantization Neural Network and LVQ algorithm. Quantum Learning Vector Quantization Neural Network is a hybrid neural network and quantum computing theory is used in the Quantum Learning Vector Quantization Neural Network directly to build Quantum Learning Vector Quantization Neural Network. Model of Quantum Learning Vector Quantization Neural Network and Quantum-LVQ algorithms are proposed. In the study of clustering problem, experiments show that Quantum Learning Vector Quantization Neural Network have an advantage over than Learning Vector Quantization Neural Network.

DOI: 10.1109/emeit.2011.6023910

Related papers

Research on Quantum Adaptive Resonance Theory Neural Network

Xuan Hou

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.

Electronic and Mechanical Engineering and Information TechnologyAug 1, 20118 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 ComputersSep 1, 20129 citations

Research on quantum gate circuit neural network and improved learning algorithm

Hou Xua

Quantum Gate Circuit Neural Network(QGCNN)is a kind of quantum neural network model, which directly uses quantum theory to design the neural network topology or training algorithms. In the neural network, Momentum update is adding momentum parameter in weight renew and provides a specific inertia while renewing weight vector. It avoids sustained oscillation of weight vector in network training. It introduces the principle of momentum update in the basic learning algorithm of Quantum Gate Circuit Neural Network, proposes Quantum Gate Circuit neural network Momentum update Algorithm(QGCMA). The research shows that QGCMA has 100% convergence rate and enhances convergence speed compared to the basic algorithm with the same learning rate.

Computer Engineering and Applications JournalJan 1, 20141 citation

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 UniversityJan 1, 20050 citations

Quantum Machine Learning

Priyanga Subbiah, N. Krishnaraj, Kiran Bellam

Machine learning improved by quantum computing. Machine learning and quantum physics fix AI and computers. This chapter discusses quantum machine learning theory, methods, and applications. Part 1 thoroughly discusses quantum and classical machine learning. The authors demonstrate how quantum supports vector machines, neural networks, and clustering speed AI. The chapter examines quantum machine learning's merits and downsides. Quantum computers optimize, parallelize, and manage huge data better. Quantum hardware restrictions and error correction reduce noise and decoherence. Explore quantum machine learning in NLP, drug discovery, financial modeling, and image recognition. Many fields could change quantum platform machine learning models with quantum algorithms. The chapter concludes with quantum machine learning directions and challenges. Check trustworthy quantum machine learning frameworks, benchmarks, and hybrid algorithms. Hot: quantum machine learning. This chapter covers fundamentals, research frameworks, and applications.

Advances in computer and electrical engineering book seriesJan 31, 20245 citations

Hybrid Quantum Neural Networks: Theory, Implementations, and Applications

Léo Monbroussou, Maniraman Periyasamy, Viacheslav Kuzmin +4 more

Artificial intelligence has been transformed by deep neural networks, yet the search for new learning architectures continues. Quantum machine learning offers one such direction, and hybrid quantum neural networks, which combine classical neural-network components with quantum information processing units, have emerged as a practical framework for near-term quantum technologies. However, the rapid development of the field across diverse architectures, benchmarks and hardware assumptions makes it difficult to assess the utility of various proposals, identify where genuine advantages may arise, and determine how practitioners can use these models. While recent benchmarks caution that such gains have not yet been demonstrated at scale, theoretical work has identified tasks on which quantum models hold provable advantages, and hybrid approaches have delivered promising results on practical problems using deliberately compact quantum components and substantially fewer trainable parameters. Here, we review hybrid quantum neural networks for the machine-learning and quantum-machine-learning communities. We summarize their main theoretical and methodological foundations, survey some of the most promising architectures developed so far, and examine their implementation challenges and reported performance. By consolidating these perspectives, this review provides a structured view of the state of the field and helps identify promising paths for future research and application-driven development.

quant-pharXiv:2608.01194Aug 4, 20260 citations