4 papers
Solving the Gross-Pitaevskii Equation with Quantic Tensor Trains: Ground States and Nonlinear Dynamics
Qian-Can Chen, I-Kang Liu, Jheng-Wei Li +1
We develop a tensor network framework based on the quantic tensor train (QTT) format to efficiently solve the Gross-Pitaevskii equation (GPE), which governs Bose-Einstein condensat…
Tailoring tensor network techniques to the quantics representation for highly inhomogeneous problems and few body problems
Jheng-Wei Li, Nicolas Jolly, Xavier Waintal
Tensor network techniques are becoming increasingly popular tools to solve partial differential equations within the so-called quantics representation. Their popularity stems from…
Entanglement across scales: Quantics tensor trains as a natural framework for renormalization
Stefan Rohshap, Jheng-Wei Li, Alena Lorenz +4
Understanding entanglement remains one of the most intriguing problems in physics. While particle and site entanglement have been studied extensively, the investigation of length o…
Learning tensor networks with tensor cross interpolation: new algorithms and libraries
Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin +8
The tensor cross interpolation (TCI) algorithm is a rank-revealing algorithm for decomposing low-rank, high-dimensional tensors into tensor trains/matrix product states (MPS). TCI…