Regensburg 2025 – scientific programme
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TT: Fachverband Tiefe Temperaturen
TT 7: Correlated Electrons: Electronic Structure Calculations
TT 7.11: Talk
Monday, March 17, 2025, 17:45–18:00, H31
SOLAX: A Python solver for fermionic quantum systems with neural network support — Louis Thirion1, Philipp Hansmann1, and •Pavlo Bilous2 — 1Department of Physics, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91058 Erlangen, Germany — 2Max Planck Institute for the Science of Light, Staudtstr. 2, 91058 Erlangen, Germany
We present a new Python library SOLAX [1] designed for configuration interaction (CI) calculations of fermionic quantum many-body systems which require high dimensional expansions in Slater determinant bases. The provided Python classes allow to conveniently encode basis sets, quantum states, and operators within the second quantization formalism. The JAX-based GPU-accelerated back-end performs efficiently the quantum mechanical operations necessary to determine many-body quantum states in finite-size Hilbert spaces.
Along with these core functionalities, SOLAX integrates a neural-network (NN) support for the CI calculation for otherwise prohibitively large expansions in Slater determinant basis sets. We show how NN can be used in SOLAX to identify a priori unknown subsets of the most important Slater determinants and iteratively obtain high-quality approximative many-body quantum states. Our recent developments include also NN-supported construction of spectral functions, which we plan to provide in future versions of SOLAX.
[1] L. Thirion, P. Hansmann, and P. Bilous, arXiv:2408.16915 (2024).
Keywords: Fermionic many-body problem; Slater determinants; Neural networks; Python; JAX