The field of Artificial Intelligence (AI) is evolving at an unprecedented pace, but concerns regarding energy consumption have been raised due to the increasing computational cost of training and deploying deep learning models. Binary Neural Networks (BiNNs) - neural networks with single-bit precision - have emerged as a promising solution, reducing memory and power requirements while maintaining competitive performance. However, their optimization remains challenging due to the limitations of traditional training algorithms. In this work, we explore the potential of Quantum HyperNetworks to enhance BiNN optimization by leveraging quantum computing. Specifically, Variational Quantum Algorithms (VQAs) facilitate the generation of binary weights through quantum circuit measurements, while quantum principles such as superposition and entanglement enable exploration of a broader solution space.