Quantum Device

Quantum devices are being actively developed for applications in quantum computing and quantum machine learning, with a primary objective of overcoming limitations imposed by noise and hardware constraints. Current research focuses on developing and optimizing algorithms like variational quantum eigensolvers (VQEs), quantum neural networks (QNNs), and quantum graph convolutional networks (QuanGCNs), often incorporating classical machine learning techniques for control, calibration, and error mitigation. These advancements are crucial for improving the accuracy and scalability of quantum computations, paving the way for practical applications in diverse fields such as materials science, drug discovery, and artificial intelligence.