Speaker
Description
Studying emergent phenomena in classical statistical physics remains one of the most computationally difficult problems. With the appropriate algorithm to renormalize the system, one of the most effective methods to study these problems is tensor networks. In the context of research areas like condensed matter, the result is a coarse grained and truncated system where only the most relevant states ranked by entropy have been maintained. An explosion of numerical algorithms which compute general properties of a statistical physics system such as specific heat, magnetization, and free energies are available; however, an overview of which tensor algorithms are best and where they must be improved would be highly advantageous for the scientific community. With our newly coded library of open access tensor network algorithms we make new recommendations of which algorithms to use, speculate on improvements for future algorithms, and provide information on how to implement novel tensor networks using our framework, the DMRjulia library.
M.R.G.F. acknowledges support from the Summer Undergraduate Research Award (SURA) from the Faculty of Science at the University of Victoria and the NSERC CREATE in Quantum Computing Program, grant number 543245.This research was undertaken, in part, thanks to funding from the Canada Research Chairs Program. We acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC). This work has been supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) under grants RGPIN-2023-05510 and DGECR- 2023-00026
| Keyword-1 | Tensor networks |
|---|---|
| Keyword-2 | Algorithms |
| Keyword-3 | Statistical physics |
