Research Data Leeds Repository

Citation

Ren, Jie (2026) Data supporting "Quantum many-body mixed phase space revealed by hybrid feedback control". University of Leeds. [Dataset] https://doi.org/10.5518/1890

Dataset description

Final source data for the quantitative main-text figures of a study of mixed phase-space dynamics and hybrid feedback control in an interacting Su-Schrieffer-Heeger ladder. The dataset contains TDVP trajectories and Poincare-section crossings, exact-diagonalization maps, and experimental imbalance and subsystem-fidelity measurements from a 24-qubit superconducting processor, deposited as plain-text CSV tables together with documentation. No code is included: the tables are exactly the data from which the figures are drawn.

Keywords:	quantum many-body dynamics; mixed phase space; hybrid feedback control; superconducting qubits; Su-Schrieffer-Heeger model; exact diagonalization; time-dependent variational principle; Poincare section; subsystem fidelity
Subjects:	F000 - Physical sciences > F300 - Physics F000 - Physical sciences > F300 - Physics > F340 - Mathematical & theoretical physics F000 - Physical sciences > F300 - Physics > F340 - Mathematical & theoretical physics > F342 - Quantum mechanics F000 - Physical sciences > F300 - Physics > F340 - Mathematical & theoretical physics > F343 - Computational physics
Divisions:	Faculty of Engineering and Physical Sciences > School of Physics and Astronomy
Related resources:	Location Type https://doi.org/10.1038/s41567-026-03431-z Publication https://doi.org/10.48550/arXiv.2607.14223 Publication https://eprints.whiterose.ac.uk/id/eprint/243527/ Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	28 Jul 2026 14:09
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1577
Additional details Additional details Creators or authors: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type Ren, Jie https://orcid.org/0000-0002-8489-1968 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: Leverhulme Trust, EPSRC, NSF Grant numbers: RL-2019-015, EP/Z533634/1, UKRI1337, PHY-2309135 Publication date: 2026 Legal and ethical issues: No human participants or personal data are included in this dataset. Resource language: English Metadata language: English Publisher: University of Leeds Copyright holders: University of Leeds Contact email: J.Ren@leeds.ac.uk Last Modified: 02 Oct 2026 14:48

Files

Documentation

README_Ren_2026.txt [6kB] README_Ren_2026.txt [6kB] README_Ren_2026.txt [6kB] Security: Anyone File name: README_Ren_2026.txt Content type: Documentation Description: Top-level README describing the dataset Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 6kB

Documentation [5kB] Documentation [5kB] Documentation [5kB] Security: Anyone File name: Documentation.zip Content type: Documentation Description: Panel-by-panel description of the source data and SHA-256 manifest of the deposited files Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 5kB

Data

Data [3MB] Data [3MB] Data [3MB] Security: Anyone File name: Data.zip Content type: Data Description: Figures data Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 3MB