arXiv subjects

Mohammad Saad

Publications and source records attributed to Mohammad Saad.

3 recordsLinked to original sources

arXiv2025

Anyon Quasilocalization in a Quasicrystalline Toric Code

An exactly solvable model of a quantum spin liquid on a quasicrystal, akin to Kitaev's honeycomb model, was introduced in Kim \textit{et al.}, \href{https://doi.org/10.1103/PhysRevB.110.214438}{\text{Phys. Rev. B} \textbf{110}, 214438 (2024)}. It was shown that in contrast to the translationally invariant models, such a spin liquid stabilizes a gapped ground state with a finite irrational flux density. In this work, we analyze the strong bond-anisotropic limit of the model and demonstrate that the aperiodic lattice geometry naturally generates a hierarchy of exponentially separated coupling constants in the resulting toric code Hamiltonian. Furthermore, a perturbative magnetic field leads to anomalous localization properties where an anyonic excitation sequentially delocalizes over subsets of sites forming equipotential contours in the quasicrystal. In addition, certain background flux configurations, together with the underlying geometry, give rise to strictly localized eigenstates that remain decoupled from the rest of the spectrum. Using numerical studies, we uncover the key mechanisms responsible for this unconventional localization behavior. Our study highlights that topologically ordered phases, in the presence of geometrical constraints can lead to highly anomalous localization properties of fractionalized charges.

cond-mat.str-el↗

arXiv2024

Had enough of experts? Quantitative knowledge retrieval from large language models

Large language models (LLMs) have been extensively studied for their abilities to generate convincing natural language sequences, however their utility for quantitative information retrieval is less well understood. Here we explore the feasibility of LLMs as a mechanism for quantitative knowledge retrieval to aid two data analysis tasks: elicitation of prior distributions for Bayesian models and imputation of missing data. We introduce a framework that leverages LLMs to enhance Bayesian workflows by eliciting expert-like prior knowledge and imputing missing data. Tested on diverse datasets, this approach can improve predictive accuracy and reduce data requirements, offering significant potential in healthcare, environmental science and engineering applications. We discuss the implications and challenges of treating LLMs as 'experts'.

cs.IR↗

arXiv2024

Quasicrystalline Spin Liquid

The interplay of electronic interactions and frustration in crystalline systems leads to a panoply of correlated phases, including exotic Mott insulators with non-trivial patterns of entanglement. Disorder introduces additional quantum interference effects that can drive localization phenomena. Quasicrystals, which are neither disordered nor perfectly crystalline, are interesting playgrounds for studying the effects of interaction, frustration, and quantum interference. Here we consider a solvable example of a quantum spin liquid on a tri-coordinated quasicrystal. We extend Kitaev's original construction for the spin model to our quasicrystalline setting and perform a large scale flux-sampling to find the ground-state configuration in terms of the emergent majorana fermions and flux excitations. This reveals a fully gapped and time-reversal symmetric quantum spin liquid, regardless of the exchange anisotropies, accompanied by a tendency towards non-trivial (de-)localization at the edge and the bulk. The advent of moir\'e materials and a variety of quantum simulators provide a new platform to bring phases of quasicrystalline quantum matter to life in a controlled fashion.

cond-mat.str-el↗