Abstract
Quantum spin liquids are exotic quantum phases of matter that do not order even at zero temperature. While there are several toy models and simple Hamiltonians that could host a quantum spin liquid as their ground state, it is very rare to find actual, realistic materials that exhibit their properties. At the same time, the classical simulation of such instances of strongly correlated systems is intricate and reliable methods are scarce. In this work, we investigate the quantum magnet Ca10Cr7O28 that has recently been discovered to exhibit properties of a quantum spin liquid in inelastic neutron scattering experiments. This compound has a distorted bilayer Kagome lattice crystal structure consisting of Cr5＋ ions with spin-1∕2 moments. Coincidentally, the lattice structure renders a tensor network algorithm in 2D applicable that can be seen as a new variant of a projected entangled simplex state algorithm in the thermodynamic limit. In this first numerical investigation of this material that takes into account genuine quantum correlations, good agreement with the experimental findings is found. Our study contributes to uplifting tensor networks from conceptual tools to methods to describe real two-dimensional quantum materials.
| Original language | English |
|---|---|
| Article number | 168292 |
| Journal | Annals of Physics |
| Volume | 421 |
| DOIs | |
| State | Published - Oct 2020 |
Funding
A. K. would like to acknowledge early discussions with Haijun Liao, Roman Orus and Thibaut Picot for technical details of the algorithm implemented here. Both J. E. and A. K. would like to acknowledge discussions with Laura Baez, Alexander Nietner and Emil Bergholtz. We would also like to thank Philippe Corboz for pointing out some of the important references on PEPS. We also acknowledge Jörg Behrmann and the HPC Service of ZEDAT, FU Berlin, for providing computing time on the cluster Curta. This work has been supported by the ERC (TAQ), the Templeton Foundation, USA , and the DFG (CRC 183 Project B1, and EI 519/15-1 , EI 519/14-1 ). This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 817482 (PASQuanS). C. B. acknowledges support from the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Scientific User Facilities . This research was also partially supported by the DFG, Germany through project B06 of SFB 1143 (project-id 247310070 ). A. K. would like to acknowledge early discussions with Haijun Liao, Roman Orus and Thibaut Picot for technical details of the algorithm implemented here. Both J. E. and A. K. would like to acknowledge discussions with Laura Baez, Alexander Nietner and Emil Bergholtz. We would also like to thank Philippe Corboz for pointing out some of the important references on PEPS. We also acknowledge J?rg Behrmann and the HPC Service of ZEDAT, FU Berlin, for providing computing time on the cluster Curta. This work has been supported by the ERC (TAQ), the Templeton Foundation, USA, and the DFG (CRC 183 Project B1, and EI 519/15-1, EI 519/14-1). This work has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 817482 (PASQuanS). C. B. acknowledges support from the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Scientific User Facilities. This research was also partially supported by the DFG, Germany through project B06 of SFB 1143 (project-id 247310070).