Universal semiclassical dynamics in disordered two-dimensional systems
- URL: http://arxiv.org/abs/2409.12956v1
- Date: Thu, 19 Sep 2024 17:59:00 GMT
- Title: Universal semiclassical dynamics in disordered two-dimensional systems
- Authors: Ćukasz Iwanek, Marcin Mierzejewski, Anatoli Polkovnikov, Dries Sels, Adam S. Sajna,
- Abstract summary: We analyze the dynamics of interacting spinless fermions propagating on disordered 1D and 2D lattices.
We find for both spatial dimensions that the imbalance exhibits a universal dependence on the rescaled time $t/xi_W$, where in 2D the time-scale $xi_W$ follows a stretched-exponential dependence on disorder strength.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The dynamics of disordered two-dimensional systems is much less understood than the dynamics of disordered chains, mainly due to the lack of appropriate numerical methods. We demonstrate that a single-trajectory version of the fermionic truncated Wigner approximation (fTWA) gives unexpectedly accurate results for the dynamics of one-dimensional (1D) systems with moderate or strong disorder. Additionally, the computational complexity of calculations carried out within this approximation is small enough to enable studies of two-dimensional (2D) systems larger than standard fTWA. Using this method, we analyze the dynamics of interacting spinless fermions propagating on disordered 1D and 2D lattices. We find for both spatial dimensions that the imbalance exhibits a universal dependence on the rescaled time $t/\xi_W$, where in 2D the time-scale $\xi_W$ follows a stretched-exponential dependence on disorder strength.
Related papers
- Recurrent Deep Kernel Learning of Dynamical Systems [0.5825410941577593]
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets.
We propose a data-driven, non-intrusive deep kernel learning (SVDKL) method to discover low-dimensional latent spaces from data.
Results show that our framework is capable of (i) denoising and reconstructing measurements, (ii) learning compact representations of system states, (iii) predicting system evolution in low-dimensional latent spaces, and (iv) modeling uncertainties.
arXiv Detail & Related papers (2024-05-30T07:49:02Z) - Validating phase-space methods with tensor networks in two-dimensional
spin models with power-law interactions [0.0]
We evaluate the dynamics of 2D power-law interacting XXZ models, implementable in a variety of state-of-the-art experimental platforms.
We compute the spin squeezing as a measure of correlations in the system, and compare to semiclassical phase-space calculations utilizing the discrete truncated Wigner approximation (DTWA)
We find the latter efficiently and accurately captures the scaling of entanglement with system size in these systems, despite the comparatively resource-intensive tensor network representation of the dynamics.
arXiv Detail & Related papers (2023-05-26T20:15:24Z) - Capturing dynamical correlations using implicit neural representations [85.66456606776552]
We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data.
In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
arXiv Detail & Related papers (2023-04-08T07:55:36Z) - From high-dimensional & mean-field dynamics to dimensionless ODEs: A
unifying approach to SGD in two-layers networks [26.65398696336828]
This manuscript investigates the one-pass gradient descent (SGD) dynamics of a two-layer neural network trained on Gaussian data and labels.
We rigorously analyse the limiting dynamics via a deterministic and low-dimensional description in terms of the sufficient statistics for the population risk.
arXiv Detail & Related papers (2023-02-12T09:50:52Z) - 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) - Semiclassical bounds on dynamics of two-dimensional interacting
disordered fermions [0.0]
We study quench dynamics of two-dimensional lattice systems consisting of interacting spinless fermions with potential disorder.
We show that the semiclassical dynamics generally relaxes faster than the full quantum dynamics.
We show that strongly disordered one-dimensional and two-dimensional systems exhibit a transient, logarithmic-in-time relaxation.
arXiv Detail & Related papers (2022-09-29T19:11:38Z) - Supersymmetric reshaping and higher-dimensional rearrangement of
photonic lattices [68.8204255655161]
We build two-dimensional (2D) systems with spectra identical to that of one-dimensional (1D) Jx lattices.
While exhibiting different dynamics, these 2D systems retain the key imaging and state transfer properties of the 1D Jx lattice.
Our method extends to other systems with separable spectra, facilitates experimental fabrication, and may increase robustness to fabrication imperfections in large-scale photonic circuits.
arXiv Detail & Related papers (2022-09-26T16:56:41Z) - Interface dynamics in the two-dimensional quantum Ising model [0.0]
We show that the dynamics of interfaces, in the symmetry-broken phase of the two-dimensional ferromagnetic quantum Ising model, displays a robust form of ergodicity breaking.
We present a detailed analysis of the evolution of these interfaces both on the lattice and in a suitable continuum limit.
The implications of our work for the classic problem of the decay of a false vacuum are also discussed.
arXiv Detail & Related papers (2022-09-19T13:08:58Z) - Calculating non-linear response functions for multi-dimensional
electronic spectroscopy using dyadic non-Markovian quantum state diffusion [68.8204255655161]
We present a methodology for simulating multi-dimensional electronic spectra of molecular aggregates with coupling electronic excitation to a structured environment.
A crucial aspect of our approach is that we propagate the NMQSD equation in a doubled system Hilbert space but with the same noise.
arXiv Detail & Related papers (2022-07-06T15:30:38Z) - Clean two-dimensional Floquet time-crystal [68.8204255655161]
We consider the two-dimensional quantum Ising model, in absence of disorder, subject to periodic imperfect global spin flips.
We show by a combination of exact diagonalization and tensor-network methods that the system can sustain a spontaneously broken discrete time-translation symmetry.
We observe a non-perturbative change in the decay rate of the order parameter, which is related to the long-lived stability of the magnetic domains in 2D.
arXiv Detail & Related papers (2022-05-10T13:04:43Z) - Simultaneous Transport Evolution for Minimax Equilibria on Measures [48.82838283786807]
Min-max optimization problems arise in several key machine learning setups, including adversarial learning and generative modeling.
In this work we focus instead in finding mixed equilibria, and consider the associated lifted problem in the space of probability measures.
By adding entropic regularization, our main result establishes global convergence towards the global equilibrium.
arXiv Detail & Related papers (2022-02-14T02:23:16Z)
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.