Pith. sign in

REVIEW 1 cited by

Decoding the drive-bath interplay: A guideline to enhance superconductivity

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.02861 v2 pith:KU3QSJNR submitted 2023-06-05 cond-mat.supr-con quant-ph

classification cond-mat.supr-conquant-ph
keywords driveninterplaysuperconductingdriven-dissipativeenhancefrequencyquantumspectral
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Driven-dissipative physics lie at the core of quantum optics. However, the full interplay between a driven quantum many-body system and its environment remains relatively unexplored in the solid state realm. In this work, we inspect this interplay beyond the commonly employed stroboscopic Hamiltonian picture based on the specific example of a driven superconductor. Using the Shirley-Floquet and Keldysh formalisms as well as a generalization of the notion of superconducting fitness to the driven case, we show how a drive which anti-commutes with the superconducting gap operator generically induces an unusual particle-hole structure in the spectral functions from the perspective of the thermal bath. Concomitant with a driving frequency which is near resonant with the intrinsic cutoff frequency of the underlying interaction, this spectral structure can be harnessed to enhance the superconducting transition temperature. Our work paves the way for further studies for driven-dissipative engineering of exotic phases of matter in solid-state systems.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AI-Driven Generation of Data Contracts in Modern Data Engineering Systems

    cs.DB 2025-05 reject novelty 4.0 of 10

    A framework that fine-tunes LLaMA-2 with LoRA to generate JSON Schema data contracts, claiming 92% field accuracy on a 500-table test set.

Pith tools