Deep Learning has demonstrated remarkable success in various domains, yet the computational demands of training large neural networks continue to pose challenges. This research investigates the integration of quantum computing principles into neural network architectures, aiming to explore and exploit the potential quantum advantages in deep learning tasks. The study focuses on Quantum Neural Networks (QNNs), where quantum bits (qubits) are leveraged to encode and process information in quantum superposition. Quantum entanglement and parallelism offer unique possibilities for enhancing the expressiveness and computational efficiency of deep learning models. We explore into the development of quantum-enhanced activation functions, weight encoding schemes, and novel layer structures adapted to quantum computing platforms. The research evaluates the performance of QNNs in comparison to classical deep neural networks across a spectrum of benchmark tasks, including image classification, natural language processing, and generative modelling. Metrics such as training convergence, model generalization, and computational speedup are analysed to measure the quantum advantage. Moreover, the investigation extends to hybrid quantum-classical approaches, exploring how classical and quantum components can simultaneously collaborate in the training and inference stages of deep learning tasks. This hybrid paradigm aims to control quantum speedup while maintaining compatibility with existing classical frameworks. This research bridges the gap between quantum computing and deep learning, providing valuable insights into the transformative potential of quantum-enhanced architectures for the next generation of AI systems. The outcomes of this study aim to guide future advancements in leveraging quantum computing for enhancing the efficiency and capabilities of deep neural networks.