Hybrid Quantum

Hybrid quantum computing focuses on integrating classical and quantum computational resources to leverage the strengths of both for enhanced performance in machine learning and other computationally intensive tasks. Current research emphasizes the development and application of hybrid quantum neural networks, including variations incorporating convolutional layers, quantum circuits, and ensemble methods, often within high-performance computing environments. These approaches aim to improve accuracy and efficiency in diverse applications such as medical image classification, drug discovery, and computational fluid dynamics, addressing limitations of purely classical or quantum approaches. The ultimate goal is to create practical, scalable hybrid systems that deliver tangible benefits across various scientific and engineering domains.