Information Sciences—Intelligent Systems: An International Journal
A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Information Sciences—Informatics and Computer Science: An International Journal
Quantum computation and quantum information
Quantum computation and quantum information
Quantum Information Processing
Model and Training of QNN with Weight
Neural Processing Letters
Quantum Computing for Computer Scientists
Quantum Computing for Computer Scientists
Quantum Logical Neural Networks
SBRN '08 Proceedings of the 2008 10th Brazilian Symposium on Neural Networks
Neurofuzzy networks with nonlinear quantum learning
IEEE Transactions on Fuzzy Systems
Neural networks with quantum architecture and quantum learning
International Journal of Circuit Theory and Applications
A Weightless Neural Node Based on a Probabilistic Quantum Memory
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Superposition Based Learning Algorithm
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Equivalence between RAM-based neural networks and probabilistic automata
IEEE Transactions on Neural Networks
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A supervised learning algorithm for quantum neural networks (QNN) based on a novel quantum neuron node implemented as a very simple quantum circuit is proposed and investigated. In contrast to the QNN published in the literature, the proposed model can perform both quantum learning and simulate the classical models. This is partly due to the neural model used elsewhere which has weights and non-linear activations functions. Here a quantum weightless neural network model is proposed as a quantisation of the classical weightless neural networks (WNN). The theoretical and practical results on WNN can be inherited by these quantum weightless neural networks (qWNN). In the quantum learning algorithm proposed here patterns of the training set are presented concurrently in superposition. This superposition-based learning algorithm (SLA) has computational cost polynomial on the number of patterns in the training set.