Abstract: Quantum Neural Networks (QNNs) represent a fascinating intersection of Quantum Computing and Artificial Neural Networks. Researchers explored combining quantum computing and neural networks in the 1990s and early 2000s. Quantum Neural Networks (QNNs) combine principles from quantum computing and artificial neural networks to potentially revolutionize machine learning. QNNs are neural networks that utilize quantum computing principles, such as superposition, entanglement, and quantum parallelism, to process and transform information. With Quantum Superposition QNNs can represent complex probability distributions, allowing for more nuanced modeling of uncertainty. While with Quantum Entanglement, QNNs can capture complex correlations between inputs, enabling more accurate modeling of complex relationships. Apart of that Quantum Parallelism facilitates QNN to process multiple inputs simultaneously, potentially speeding up certain computations.