Regensburg 2025 – wissenschaftliches Programm
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TT: Fachverband Tiefe Temperaturen
TT 37: Correlated Electrons: Poster
TT 37.52: Poster
Mittwoch, 19. März 2025, 15:00–18:00, P4
Computing Excited States in Quantum Many-Body Clusters with Neural Network Support — •Max Kroesbergen1, Louis Thirion1, Gianluca Levi3, Pavlo Bilous2, Paul Fadler1, Yorick Schmerwitz3, 4, Elvar Ö. Jónsson3, Hannes Jónsson3, and Philipp Hansmann1 — 1Friedrich-Alexander-Universität Erlangen-Nürnberg — 2Max Planck Institute for the Science of Light, Erlangen — 3University of Iceland, Reykjavik — 4Max Planck Institute for Coal Research, Mühlheim
In this study, we used SOLAX [1], our newly developed Python library for configuration interaction (CI) calculations of fermionic quantum systems, to compute the energies of ground and excited states for various quantum clusters. SOLAX can leverage the power of a neural network (NN) classifier to perform selective CI which mitigates the exponential growth of the many-body Hilbert space. Our benchmarks indicate a significant boost in computational efficiency while maintaining high accuracy. We validate our method for the (discrete) Single Impurity Anderson Model [2] as well as molecular systems, such as N2 and H2 [3]. For the latter, we study the dissociation curves of ground- and excited states and their dependence on the underlying single-particle basis, including Hartree-Fock orbitals optimized for excited states. Additionally, SOLAX enables us to simulate spectral functions defined by a transition operator, providing deeper insights into the excitation dynamics of these systems.
[1] L. Thirion, P. Hansmann, P. Bilous, arXiv:2408.16915v1.
[2] P. Bilous et al., arXiv:2406.00151.
[3] Y. L. A. Schmerwitz et al., arXiv:2406.08154.
Keywords: configuration interaction; machine learning; neural network classifier; Python