Quantum Many-Body Physics
Systems composed of multiple quantum components exhibit rich physical phenomena and can give rise to the most interesting macroscopic properties (e.g. high-temperature superconductivity or thermalization). Most such systems are very hard to solve, and it is of fundamental interest to develop techniques that allow us a better understanding of these phenomena.
The Theory Division investigates these problems from different perspectives:
We apply the new methods to physically interesting problems, which are hard to approach with other techniques.
Combining Tensor Network with other numerical techniques (e.g. Monte Carlo sampling, machine learning methods), new numerical algorithms are designed that allow us to tackle the most challenging problems.
We develop theories and variational ansatzes for bosonic and fermionic systems that extends Gaussian states to interacting systems.
Tensor network theory.—Tensor Networks offer the possibility to construct states where collective properties are determined from small local tensors.