A causality-based divide-and-conquer algorithm for nonequilibrium Green’s function calculations with quantics tensor trains, by Ken Inayoshi, Maksymilian Środa, Anna Kauch, Philipp Werner, Hiroshi Shinaoka
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SciPost Physics Home Authoring Refereeing Submit a manuscript About A causality-based divide-and-conquer algorithm for nonequilibrium Green’s function calculations with quantics tensor trains Ken Inayoshi, Maksymilian Środa, Anna Kauch, Philipp Werner, Hiroshi Shinaoka SciPost Phys. 20, 077 (2026) · published 9 March 2026 doi: 10.21468/SciPostPhys.20.3.077 pdf BiBTeX RIS Submissions/Reports Abstract We propose a causality-based divide-and-conquer algorithm for nonequilibrium Green's function calculations with quantics tensor trains. This algorithm enables stable and efficient extensions of the simulated time domain by exploiting the causality of Green's functions. We apply this approach within the framework of nonequilibrium dynamical mean-field theory to the simulation of quench dynamics in symmetry-broken phases, where long-time simulations are often required to capture slow relaxation dynamics. We demonstrate that our algorithm allows to extend the simulated time domain without a significant increase in the cost of storing the Green's function. × TY - JOURPB - SciPost FoundationDO - 10.21468/SciPostPhys.20.3.077TI - A causality-based divide-and-conquer algorithm for nonequilibrium Green’s function calculations with quantics tensor trainsPY - 2026/03/09UR - https://scipost.org/SciPostPhys.20.3.077JF - SciPost PhysicsJA - SciPost Phys.VL - 20IS - 3SP - 077A1 - Inayoshi, KenAU - Środa, MaksymilianAU - Kauch, AnnaAU - Werner, PhilippAU - Shinaoka, HiroshiAB - We propose a causality-based divide-and-conquer algorithm for nonequilibrium Green's function calculations with quantics tensor trains. This algorithm enables stable and efficient extensions of the simulated time domain by exploiting the causality of Green's functions. We apply this approach within the framework of nonequilibrium dynamical mean-field theory to the simulation of quench dynamics in symmetry-broken phases, where long-time simulations are often required to capture slow relaxation dynamics. We demonstrate that our algorithm allows to extend the simulated time domain without a significant increase in the cost of storing the Green's function.ER - × @Article{10.21468/SciPostPhys.20.3.077, title={{A causality-based divide-and-conquer algorithm for nonequilibrium Green’s function calculations with quantics tensor trains}}, author={Ken Inayoshi and Maksymilian Środa and Anna Kauch and Philipp Werner and Hiroshi Shinaoka}, journal={SciPost Phys.}, volume={20}, pages={077}, year={2026}, publisher={SciPost}, doi={10.21468/SciPostPhys.20.3.077}, url={https://scipost.org/10.21468/SciPostPhys.20.3.077},} Disclosure of Generative AI use The author(s) disclose that the following generative AI tools have been used in the preparation of this publication: In the main text, GitHub Copilot in VS Code (ChatGPT-4.1) was used for spelling and grammar checking. Ontology / Topics See full Ontology or Topics database. Green's functions Tensor networks Authors / Affiliations: mappings to Contributors and Organizations See all Organizations. 1 Ken Inayoshi, 2 Maksymilian Środa, 3 Anna Kauch, 2 Philipp Werner, 1 Hiroshi Shinaoka 1 埼玉大学 / Saitama University 2 Université de Fribourg / University of Fribourg 3 Technische Universität Wien / Vienna University of Technology [TUW] Funders for the research work leading to this publication Austrian Science Fund (FWF) (through Organization: Fonds zur Förderung der wissenschaftlichen Forschung / FWF Austrian Science Fund [FWF]) Japan Science and Technology Agency [JST] 日本学術振興会 / Japan Society for the Promotion of Science [JSPS] Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung / Swiss National Science Foundation [SNF]