Quantum artificial neural network architectures and components
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
Approximation by fully complex multilayer perceptrons
Neural Computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Binary Neuro-Fuzzy Classifiers Trained by Nonlinear Quantum Circuits
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
A New Method of Image Compression Based on Quantum Neural Network
ISME '10 Proceedings of the 2010 International Conference of Information Science and Management Engineering - Volume 01
Quantum Neural Network Algorithm Based on Multi-agent in Target Fusion Recognition System
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
Image Restoration Using a Multilayered Quantum Backpropagation Neural Network
CICN '11 Proceedings of the 2011 International Conference on Computational Intelligence and Communication Networks
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
A feedforward artificial neural network based on quantum effect vector-matrix multipliers
IEEE Transactions on Neural Networks
A quantum-inspired evolutionary algorithm for optimization numerical problems
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning.