Many Body

Many-body physics focuses on understanding the behavior of systems with numerous interacting particles, a challenge amplified by the exponential scaling of computational complexity with system size. Current research heavily utilizes machine learning, employing architectures like tensor networks, neural networks (including transformers and recurrent networks), and kernel methods to efficiently approximate quantum states, predict properties, and learn Hamiltonian dynamics. These advancements are crucial for simulating complex materials, designing quantum technologies, and accelerating scientific discovery across diverse fields, from condensed matter physics to chemistry and materials science.