Zobrazeno 1 - 10
of 2 286
pro vyhledávání: '"Müller Tobias"'
We employ a family of ancilla qubit variational wave-functions [Zhang and Sachdev, Phys. Rev. Res. 2, 023172 (2020)] to describe the polaronic correlations in the pseudo-gap metal phase of a hole-doped 2D Fermi-Hubbard model. Comparison to ultra-cold
Externí odkaz:
http://arxiv.org/abs/2408.01492
In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections with predic
Externí odkaz:
http://arxiv.org/abs/2402.13666
Autor:
Rometsch, Thomas, Jordan, Lucas M., Moldenhauer, Tobias W., Wehner, Dennis, Restrepo, Steven Rendon, Müller, Tobias W. A., Picogna, Giovanni, Kley, Wilhelm, Dullemond, Cornelis P.
Context: Planet-disk interactions play a crucial role in the understanding of planet formation and disk evolution. There are multiple numerical tools available to simulate these interactions, including the often-used FARGO code and its variants. Many
Externí odkaz:
http://arxiv.org/abs/2401.16203
Autor:
Beck, Jonas, Bodky, Jonathan, Motruk, Johannes, Müller, Tobias, Thomale, Ronny, Ghosh, Pratyay
Publikováno v:
Phys. Rev. B 109, 184422 (2024)
We microscopically analyze the nearest neighbor Heisenberg model on the maple-leaf lattice through neural quantum states (NQS) and infinite density matrix renormalization group (iDMRG). Embarking to parameter regimes beyond the exact dimer singlet gr
Externí odkaz:
http://arxiv.org/abs/2401.04995
Autor:
Niggemann, Nils, Astrakhantsev, Nikita, Ralko, Arnaud, Ferrari, Francesco, Maity, Atanu, Müller, Tobias, Richter, Johannes, Thomale, Ronny, Neupert, Titus, Reuther, Johannes, Iqbal, Yasir, Jeschke, Harald O.
Publikováno v:
Phys. Rev. B 108, L241117 (2023)
$\require{mhchem}$The square-kagome lattice Heisenberg antiferromagnet is a highly frustrated Hamiltonian whose material realizations have been scarce. We theoretically investigate the recently synthesized $\ce{Na6Cu7BiO4(PO4)4Cl3}$ where a Cu$^{2+}$
Externí odkaz:
http://arxiv.org/abs/2310.05219
Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (Fe
Externí odkaz:
http://arxiv.org/abs/2308.02454