One-dimensional quasicrystals with tensor-network finite-state automata

Abstract:Quasicrystals occupy a distinctive position between the translational order of crystals and the disordered amorphous matter. Simulating this physics has remained challenging, since quasiperiodic structures lack the translational symmetry exploited for crystals and generally require costly diagonalization of large finite approximants. Quasicrystalline order admits two equivalent descriptions, a cut-and-project scheme from a higher-dimensional periodic crystal, and a discrete set of substitution rules acting on a finite alphabet. We show that the latter, written in a numeration system adapted to the substitution, defines a deterministic finite automaton with output, the digits of a site index are fed, and the automaton returns the letter occupying that site. We further exploit another equivalence to a different construction, the transition matrices are exactly the tensors of a matrix product state, whose bond dimension is the number of automaton states and is independent of system size. This allows efficient representation of extremely large tight-binding Hamiltonians in the tensor-train language, thereby yielding an exact matrix product operator for the quasicrystal Hamiltonian at any system size. We show how this framework works for two families of one-dimensional quasicrystals, the metallic-mean and $k$-bonacci families and we explicitly construct the Fibonacci, silver-mean, and Tribonacci quasicrystals. By leveraging efficient tensor-network compression and the kernel polynomial method, we compute spectral densities for chains with more than $10^9$ sites and directly resolve the hierarchical structure of the spectrum.

17 pages (8 main text, 9 appendix), 5 figures (3 main text, 2 appendix). Submission to Physical Review B

Mesoscale and Nanoscale Physics (cond-mat.mes-hall); Computational Physics (physics.comp-ph); Quantum Physics (quant-ph)

arXiv:2609.06040 [cond-mat.mes-hall]

(or arXiv:2609.06040v1 [cond-mat.mes-hall] for this version)

https://doi.org/10.48550/arXiv.2609.06040 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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