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Binary Neural Networks (BNNs) are the result of a simplification of network parameters in Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases significantly as the size of the network increases. This complexity can be greatly reduced if the parameters of the network are binarized. Binarization, which is a one bit quantization, can also come with complications including error and information loss. The implementation of BNNs on quantum hardware could potentially provide a computational advantage over its classical counterpart. This is due to the fact that binarized parameters fit nicely to the nature of quantum hardware. Quantum superposition allows the network to be trained more efficiently, without using back propagation techniques, with the application of Grover’s Algorithm for the training process. This paper looks into two BNN designs that utilize only quantum hardware, as opposed to hybrid quantum-classical implementations. It also provides practical implementations for both of them. Looking into their scalability, improvements on the design are proposed to reduce complexity even further.