Tensor Network Methods

Tensor Network Methods are powerful computational techniques used to represent and analyze high-dimensional quantum many-body systems efficiently. Instead of storing exponentially large wavefunctions explicitly, tensor networks decompose them into interconnected tensors with controlled complexity. Common tensor network structures include matrix product states, tree tensor networks, and projected entangled pair states. These methods exploit the fact that physically relevant quantum states often have limited entanglement. Tensor network methods have become central to condensed matter physics, quantum information, and quantum chemistry. They allow accurate calculation of ground states, low-energy excitations, and dynamical properties. Tensor networks also provide insight into entanglement structure and quantum correlations. Their scalability makes them suitable for large systems that are otherwise intractable. Tensor network methods bridge numerical efficiency with deep physical interpretation, making them indispensable in modern quantum many-body physics.