Localization from Infinitesimal Kinetic Grading: Critical Scaling and Kibble-Zurek Universality
- URL: http://arxiv.org/abs/2512.15795v1
- Date: Tue, 16 Dec 2025 17:26:06 GMT
- Title: Localization from Infinitesimal Kinetic Grading: Critical Scaling and Kibble-Zurek Universality
- Authors: Argha Debnath, Ayan Sahoo, Debraj Rakshit,
- Abstract summary: We study a one-dimensional lattice model with site-dependent nearest-neighbor hopping amplitudes that follow a power-law profile.<n>In the thermodynamic limit, the ground state becomes localized as $|| to 0$, signaling the presence of a critical point characterized by a diverging localization length.<n>Our results demonstrate a clean, disorder-free route to localization and provide a tunable platform relevant to photonic lattices and ultracold atom arrays with engineered hopping profiles.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study a one-dimensional lattice model with site-dependent nearest-neighbor hopping amplitudes that follow a power-law profile. The hopping variation is controlled by a grading exponent, $α$, which serves as the tuning parameter of the system. In the thermodynamic limit, the ground state becomes localized as $|α| \to 0$, signaling the presence of a critical point characterized by a diverging localization length. Using exact diagonalization, we perform finite-size scaling analysis and extract the associated critical exponent governing this divergence, revealing a universality class distinct from well-known Anderson, Aubry-Andre, and Stark localization. To further characterize the critical behavior, we analyze the inverse participation ratio, the energy gap between the ground and first excited states, and the fidelity susceptibility. We also investigate nonequilibrium dynamics by linearly ramping the hopping profile at various rates and tracking the evolution of the localization length and the inverse participation ratio. The Kibble-Zurek mechanism successfully captures the resulting dynamics using the critical exponents obtained from the static scaling analysis. Our results demonstrate a clean, disorder-free route to localization and provide a tunable platform relevant to photonic lattices and ultracold atom arrays with engineered hopping profiles.
Related papers
- On the Mechanism and Dynamics of Modular Addition: Fourier Features, Lottery Ticket, and Grokking [49.1352577985191]
We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task.<n>Our work provides a full mechanistic interpretation of the learned model and a theoretical explanation of its training dynamics.
arXiv Detail & Related papers (2026-02-18T20:25:13Z) - Anderson localisation in spatially structured random graphs [0.0]
We study Anderson localisation on high-dimensional graphs with spatial structure induced by long-ranged but distance-dependent hopping.<n>We introduce a class of models that interpolate between the short-range Anderson model on a random regular graph and fully connected models with statistically uniform hopping.
arXiv Detail & Related papers (2026-01-01T05:55:42Z) - Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization [48.25304391127552]
Estimating the tangent spaces of a data manifold is a fundamental problem in data analysis.<n>We propose a method, Laplacian Eigenvector Gradient Orthogonalization (LEGO), that utilizes the global structure of the data to guide local tangent space estimation.
arXiv Detail & Related papers (2025-10-02T17:59:45Z) - Quantum criticality and Kibble-Zurek scaling in the Aubry-André-Stark model [0.0]
We explore quantum criticality and Kibble-Zurek scaling (KZS) in the Aubry-Andre-Stark (AAS) model.
We perform scaling analysis and numerical calculations of the localization length, inverse participation ratio (IPR), and energy gap.
arXiv Detail & Related papers (2024-05-16T15:40:55Z) - Transformers Learn Nonlinear Features In Context: Nonconvex Mean-field Dynamics on the Attention Landscape [40.78854925996]
Large language models based on the Transformer architecture have demonstrated impressive ability to learn in context.
We show that a common nonlinear representation or feature map can be used to enhance power of in-context learning.
arXiv Detail & Related papers (2024-02-02T09:29:40Z) - Localization with non-Hermitian off-diagonal disorder [0.0]
We discuss a non-Hermitian system governed by random nearest-neighbour tunnellings.<n>A physical situation of completely real eigenspectrum arises owing to the Hamiltonian's tridiagonal matrix structure.<n>The off-diagonal disorder leads the non-Hermitian system to a delocalization-localization crossover in finite systems.
arXiv Detail & Related papers (2023-10-20T18:02:01Z) - Machine learning in and out of equilibrium [58.88325379746631]
Our study uses a Fokker-Planck approach, adapted from statistical physics, to explore these parallels.
We focus in particular on the stationary state of the system in the long-time limit, which in conventional SGD is out of equilibrium.
We propose a new variation of Langevin dynamics (SGLD) that harnesses without replacement minibatching.
arXiv Detail & Related papers (2023-06-06T09:12:49Z) - Slow semiclassical dynamics of a two-dimensional Hubbard model in
disorder-free potentials [77.34726150561087]
We show that introduction of harmonic and spin-dependent linear potentials sufficiently validates fTWA for longer times.
In particular, we focus on a finite two-dimensional system and show that at intermediate linear potential strength, the addition of a harmonic potential and spin dependence of the tilt, results in subdiffusive dynamics.
arXiv Detail & Related papers (2022-10-03T16:51:25Z) - Localization of a mobile impurity interacting with an Anderson insulator [0.0]
We study a mobile impurity, representing a small quantum bath, that interacts locally with an Anderson insulator with a finite density of localized particles.
Using an extension of the density matrix renormalization group algorithm to excited states (DMRG-X), we approximate the highly excited eigenstates of the system.
We find that the impurity remains localized in the eigenstates and entanglement is enhanced in a finite region around the position of the impurity.
arXiv Detail & Related papers (2021-11-16T16:39:28Z) - Detecting delocalization-localization transitions from full density
distributions [0.0]
Characterizing the delocalization transition in closed quantum systems with a many-body localized phase is a key open question in the field of nonequilibrium physics.
We study its scaling behavior across delocalozation transitions and identify critical points from scaling collapses of numerical data.
We observe a distinctively different scaling behavior in the case of interacting fermions with random disorder consistent with a Kosterlitz-Thouless transition.
arXiv Detail & Related papers (2021-05-21T21:39:27Z) - Localisation in quasiperiodic chains: a theory based on convergence of
local propagators [68.8204255655161]
We present a theory of localisation in quasiperiodic chains with nearest-neighbour hoppings, based on the convergence of local propagators.
Analysing the convergence of these continued fractions, localisation or its absence can be determined, yielding in turn the critical points and mobility edges.
Results are exemplified by analysing the theory for three quasiperiodic models covering a range of behaviour.
arXiv Detail & Related papers (2021-02-18T16:19:52Z) - Driven-dissipative Ising Model: An exact field-theoretical analysis [0.0]
Driven-dissipative many-body systems are difficult to analyze analytically due to their non-equilibrium dynamics, dissipation and many-body interactions.
We develop an exact field-theoretical analysis and a diagrammatic representation of the spin model that can be understood from a simple scattering picture.
arXiv Detail & Related papers (2021-01-13T19:00:21Z) - Multiplicative noise and heavy tails in stochastic optimization [62.993432503309485]
empirical optimization is central to modern machine learning, but its role in its success is still unclear.
We show that it commonly arises in parameters of discrete multiplicative noise due to variance.
A detailed analysis is conducted in which we describe on key factors, including recent step size, and data, all exhibit similar results on state-of-the-art neural network models.
arXiv Detail & Related papers (2020-06-11T09:58:01Z) - Continuous-time quantum walks in the presence of a quadratic
perturbation [55.41644538483948]
We address the properties of continuous-time quantum walks with Hamiltonians of the form $mathcalH= L + lambda L2$.
We consider cycle, complete, and star graphs because paradigmatic models with low/high connectivity and/or symmetry.
arXiv Detail & Related papers (2020-05-13T14:53:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.