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The main highlight of DHNN is the present of content associative memory (CAM) whereby it is used to store the pattern of the problem and it consists of interconnected neurons with no hidden layer which act as the potential characteristics for solving optimization problems (Guo, et al., 2022).

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 Hopfield Network. DHNN can dhnnlearn (memorize) patterns and remember (recover) the patterns when the network feeds those with noises.

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