Intermediate Scale Quantum

Noisy Intermediate-Scale Quantum (NISQ) computing focuses on leveraging current quantum hardware, despite its limitations, for practical applications. Research heavily emphasizes developing and improving quantum machine learning (QML) algorithms, including variational quantum circuits (VQCs), quantum neural networks (QNNs), and quantum reservoir computing (QRC), addressing challenges like noise mitigation and efficient training. These efforts aim to demonstrate quantum advantage in areas such as medicine, finance, and materials science by enhancing the performance and reliability of QML models in noisy environments, ultimately bridging the gap between theoretical quantum computing and real-world applications.