Hybrid Quantum Classical

Hybrid quantum-classical computing is a rapidly developing field aiming to leverage the strengths of both quantum and classical computational paradigms for machine learning tasks. Current research focuses on developing and testing hybrid models, incorporating variational quantum circuits, quantum kernels, and quantum-enhanced classical algorithms like Newton's method, within architectures such as convolutional neural networks and generative adversarial networks. These efforts seek to improve performance metrics like accuracy, training speed, and model interpretability for various applications, including image classification, natural language processing, and optimization problems. The ultimate goal is to identify and demonstrate clear quantum advantages in specific problem domains, bridging the gap between theoretical potential and practical implementation.