Quantum Framework

Quantum frameworks are being developed to leverage quantum computing's potential for solving complex problems currently intractable for classical computers. Current research focuses on applying quantum algorithms and architectures, such as variational quantum algorithms and quantum neural networks, to machine learning tasks like image classification and reinforcement learning, often incorporating classical components for hybrid approaches. These efforts aim to improve the efficiency and accuracy of existing machine learning methods and enable new capabilities in areas such as quantum error correction and the study of quantum systems, with ongoing work addressing challenges like training optimization and verification of quantum computations.