Matrix-product-state-based band-Lanczos solver for quantum cluster approaches
Sebastian Paeckel, Thomas K\"ohler, Salvatore R. Manmana, Benjamin Lenz

TL;DR
This paper introduces an MPS-based band-Lanczos solver for quantum cluster methods, enabling the treatment of larger clusters beyond exact diagonalization, with improved accuracy and applicability to complex models.
Contribution
The paper develops a novel MPS-based cluster solver with continuous energy truncation and robust convergence, extending quantum cluster method capabilities beyond traditional limits.
Findings
Successfully applied to the Hubbard model at half filling.
Achieved accurate extrapolation of observables with large clusters.
Demonstrated applicability to complex materials like CaCuO$_2$.
Abstract
We present matrix-product state (MPS) based band Lanczos method as solver for quantum cluster methods such as the variational cluster approximation. While a na\"ive implementation of MPS as cluster solver would barely improve its range of applicability, we show that our approach makes it possible to treat cluster geometries well beyond the reach of exact diagonalization methods. The key modifications we introduce are a continuous energy truncation combined with a convergence criterion that is more robust against approximation errors introduced by the MPS representation and provides a bound to deviations in the resulting Green's function. The potential of the resulting cluster solver is demonstrated by computing the self-energy functional for the single-band Hubbard model at half filling in the strongly correlated regime, on different cluster geometries. Here, we find that only when…
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Taxonomy
TopicsPhysics of Superconductivity and Magnetism · Quantum many-body systems · Theoretical and Computational Physics
