The excitation ansatz for tensor networks is a powerful tool for simulating the low-lying quasiparticle excitations above ground states of strongly correlated quantum many-body systems. Recently, the two-dimensional tensor network class of infinite entangled pair states gained new ground state optimization methods based on automatic differentiation, which are at the same time highly accurate and simple to implement. Naturally, the question arises whether these new ideas can also be used to optimize the excitation ansatz, which has recently been implemented in two dimensions as well. In this paper, we describe a straightforward way to reimplement the framework for excitations using automatic differentiation, and demonstrate its performance for the Hubbard model at half filling.
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Authors / Affiliations: mappings to Contributors and OrganizationsSee all Organizations.
- 1 Institute of Physics, University of Amsterdam [IoP, UvA]
- 2 Würzburg-Dresden Cluster of Excellence [ct.qmat]
- Deutsche Forschungsgemeinschaft / German Research FoundationDeutsche Forschungsgemeinschaft [DFG]
- European Research Council [ERC]
- Ministerie van Onderwijs, Cultuur en Wetenschap / Ministry of Education Culture and Science [OCW]
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek / Netherlands Organisation for Scientific Research [NWO]