Tensor network methods provide powerful theoretical and numerical tools for studying quantum many-body systems. They offer an efficient representation of quantum states with complex entanglement structures and significantly reduce the exponential computational complexity typically associated with many-body problems. The density matrix renormalization group (DMRG) is one of the most successful and widely used tensor network algorithms.

In recent years, tensor network methods have found broad applications in condensed matter physics, quantum information science, quantum chemistry, and machine learning. My research employs various tensor network algorithms to investigate complex quantum many-body systems, with a particular focus on the numerical simulation and physical characterization of topological quantum states and their phase transitions.