Quantum Neural Network

Quantum neural networks (QNNs) leverage quantum computing principles to enhance machine learning capabilities, primarily aiming to improve the speed, accuracy, and efficiency of various learning tasks. Current research focuses on developing novel QNN architectures, such as continuous-time recurrent and liquid QNNs, and exploring efficient training algorithms to overcome challenges like barren plateaus, often employing techniques like swarm optimization or knowledge distillation from classical networks. This field is significant because QNNs offer the potential for quantum advantage in diverse applications, including image classification, power system simulations, and genomic data analysis, although the extent of this advantage and the practical scalability remain active areas of investigation.