CNOT-Measure Quantum Neural Networks

Cortico-Hippocampal Computational Modeling Using Quantum Neural Networks to Simulate Classical Conditioning Paradigms

“…Topologies such as feedforward, recurrent, convolutional, classical, and deep neural networks have been used with various modifications in different applications. The learning process of an ANN is based on the optimization of an assigned performance function using a sequence of iterations to map the output vectors to the related inputs [ 2 , 3 ].…”

“…Researchers have proposed various models using ANNs to mimic some specific regions of the brain [ 4 ]. They validated their proposed models with empirical biological experiments using conditioned stimulus (CS), unconditioned stimulus (US), and conditioned response (CR) [ 3 , 5 ]. Such models use the powerful ability of ANNs to test brain activities with unprecedentedly strong tendencies in humans and animals [ 6 , 7 ].…”

Cortico-Hippocampal Computational Modeling Using Quantum-Inspired Neural Networks

“…Although there is no experimental evidence that real neurons have specific features in common with QNN models (that are not already included in classical models), adding quantum computing features to artificial neurons can significantly enhance the computational capability of classical neural networks (da Silva and de Oliveira, 2016 ). The addition of quantum computing features to ANNs has been found to speed up the learning process (Xu et al, 2011 ; Altaher and Taha, 2017 ; Lukac et al, 2018 ). Thus, QINNs have greater computational capability than classical networks (Zhou, 2010 ).…”