Quantum Process

Quantum process tomography aims to characterize unknown quantum operations, a crucial task for advancing quantum technologies. Current research focuses on developing efficient algorithms, such as Riemannian gradient descent and tensor networks, to reconstruct these processes from experimental data, often leveraging machine learning techniques like reinforcement learning and neural networks for optimization and error mitigation. These advancements are vital for improving the accuracy and scalability of quantum computing, enabling better characterization of quantum devices and facilitating the development of more robust quantum algorithms. Furthermore, efficient quantum process learning is essential for applications ranging from quantum cryptography to the analysis of complex quantum systems.