Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Information Sciences—Informatics and Computer Science: An International Journal
Simulations of quantum neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
Shadows of the Mind: A Search for the Missing Science of Consciousness
Shadows of the Mind: A Search for the Missing Science of Consciousness
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Quantum computation and quantum information
Quantum computation and quantum information
Morphogenic neural networks encode abstract rules by data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Fuzzy Stochastic Automata for Reactive Learning and Hybrid Control
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator
Open Systems & Information Dynamics
Parallelization of a fuzzy control algorithm using quantum computation
IEEE Transactions on Fuzzy Systems
Bipolar spectral associative memories
IEEE Transactions on Neural Networks
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Neural Processing Letters
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Quantum associative memories are connectionist structures that demonstrate the particle-wave nature of information and are compatible with quantum mechanics postulates. Following the solution of Schrödinger's diffusion equation, and using the Hopfield memory model, quantum associative memories are developed. It is proved that the weight matrix of quantum associative memories can be decomposed in a superposition of matrices, thus resulting in an exponential increase of the number of attractors (memory patterns). The storage and recall of patterns in quantum associative memories is studied through a numerical example and simulation tests.