Chebyshev matrix product state approach for spectral functions
Andreas Holzner, Andreas Weichselbaum, Ian P. McCulloch and, Ulrich Schollw\"ock, Jan von Delft

TL;DR
This paper introduces Chebyshev matrix product state (CheMPS), a numerically efficient method for calculating spectral functions of 1D lattice models, offering uniform resolution, controlled broadening, and reduced computational cost.
Contribution
The paper develops CheMPS, a novel approach combining Chebyshev expansions with MPS, providing a scalable and accurate way to compute spectral functions with bounded entanglement growth.
Findings
CheMPS achieves spectral resolution comparable to correction vector DMRG.
It agrees well with Bethe Ansatz results for infinite systems.
CheMPS can be applied in the time domain as an alternative to time-dependent DMRG.
Abstract
We show that recursively generated Chebyshev expansions offer numerically efficient representations for calculating zero-temperature spectral functions of one-dimensional lattice models using matrix product state (MPS) methods. The main features of this Chebychev matrix product state (CheMPS) approach are: (i) it achieves uniform resolution over the spectral function's entire spectral width; (ii) it can exploit the fact that the latter can be much smaller than the model's many-body bandwidth; (iii) it offers a well-controlled broadening scheme; (iv) it is based on a succession of Chebychev vectors |t_n>, (v) whose entanglement entropies were found to remain bounded with increasing recursion order n for all cases analyzed here; (vi) it distributes the total entanglement entropy that accumulates with increasing n over the set of Chebyshev vectors |t_n>. We present zero-temperature CheMPS…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
