In this study, we investigate and implement a discrete Hopfield neural network (DHNN) using SFQ circuits. A DHNN is a binary neural network with less information than a standard full precision neural network; it also provides a higher processing speed.
DHNN is a minimalistic and Numpy based implementation of the Discrete Hopfdhnnield Network. DHNN can learn (memorize) patterns and remember (recover) the patterns when the network feeds those with noises.
DHNN is a simple structure with no hidden layer and binary input/output which leads to lower complexity compared to other networks. Satisfiability as a neuron representation is considered a suitable logical structure due to its simple construction.
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