TRANSMISSION // 2604.21992v2
Periodic driving reshapes mobility edges in a quasiperiodic model
Source published
A theoretical study reports that a periodic electric field can tune mobility edges and transport in a generalized Aubry–André model. The abstract describes two types of boundary between quantum states, with different transport classifications in two regimes. It also reports that low-frequency driving can depart significantly from the high-frequency description. This account is based on the abstract rather than a full examination of the paper.
Can a periodically varying electric field alter which quantum states are localized and how transport proceeds? Jayashis Das and colleagues investigate this question in a quasiperiodic lattice model, focusing on how its mobility edges respond to the drive.
A mobility edge marks a boundary between different kinds of quantum states. In this study, researchers examine how such boundaries behave when the generalized Aubry–André model—also called the Ganeshan–Pixley–Das Sarma (GPD) model—is subjected to a periodic electric field. The model is already known to host mobility edges; the question is how driving changes its spectrum and transport.
The authors analyze the driven system’s Floquet spectrum, which describes its behavior under periodic time variation. According to the abstract, they find two distinct Floquet mobility edges. In the model’s bounded regime, a delocalized–localized (DL) edge separates delocalized states from localized states. In its unbounded regime, a multifractal–localized (ML) edge separates multifractal states from localized ones. The abstract names these categories but does not explain their detailed spatial structure.
For high-frequency driving, the researchers map the effective description to the static GPD model with hopping renormalized by a Bessel function. From this mapping, they obtain formulas for the driven mobility edges. The abstract also reports drive-induced localization at particular parameter values, where effective tunneling is coherently suppressed. The authors use numerical diagnostics including fractal dimension and inverse participation ratio to characterize states, and report that the electric field’s amplitude and frequency can control the mobility edges.
The abstract also reports different transport classifications in the two regimes: superdiffusive to almost ballistic transport in the bounded regime associated with the DL edge, and subdiffusive transport in the unbounded regime associated with the ML edge. These are the authors’ reported findings; the abstract does not provide enough methodological detail to assess how the classifications were established.
Frequency is an important qualification. The high-frequency description does not capture all the behavior examined: the authors report significant and counterintuitive deviations in spectral properties and transport at low frequencies. The abstract does not provide enough detail to assess the scale or causes of those deviations.
This account is based on the abstract because full paper extraction was unavailable. It therefore cannot assess the complete methods, calculations, or supporting evidence. The work is theoretical and does not report an experimental demonstration. It is an arXiv preprint; this summary does not establish whether it has been peer reviewed or independently validated. The authors’ central proposal is that periodic driving offers a way to tune mobility edges and transport in this model, with results depending on the driving-frequency regime.
- Floquet physics
- mobility edges
- quantum localization
- quasiperiodic models
- quantum transport
Reviewed cross-references
These connections were reviewed by a model, not independently fact-checked by a human. Similarity does not imply agreement; consult the original papers and the evidence below.
A driven superconducting circuit could correct its own quantum errors
Shared method: Both studies use periodic driving and Floquet effective Hamiltonians: the GPD work derives a high-frequency effective model with Bessel-renormalized hopping, while the circuit proposal uses drive parameters to obtain an effective GKP stabilizer Hamiltonian. This is a shared theoretical framework, not evidence that the distinct drive protocols or predicted outcomes are equivalent; both reports are theoretical.
Model review: · openai-codex/gpt-6-luna
Evidence and provenance
Selected excerpts, not complete papers. PDF extraction can omit figures or damage equations, tables and reading order; sources may be preprints.
2604.21992v2 — Original paper
Excerpt reference:
static/papers/2604.21992v2.md:57-107
Basis: Paper text
Source SHA-256:13c4ff601c7e922e05f2cab85091838a217c3ae5ad560e634d99f910529c4a172412.03650v2 — Original paper
Excerpt reference:
static/papers/2412.03650v2.md:12-71
Basis: Paper text
Source SHA-256:2ce38a9c8a0d6a8f4663b3f82cd0ea452872a88098e1208b35ff925bd2592b532412.03650v2 — Original paper
Excerpt reference:
static/papers/2412.03650v2.md:72-133
Basis: Paper text
Source SHA-256:2ce38a9c8a0d6a8f4663b3f82cd0ea452872a88098e1208b35ff925bd2592b53
Related reading
Automatically matched by shared topic keywords. These links do not imply that the sources agree or establish a consensus.