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Controlling emergent dynamical behavior via phase-engineered strong symmetries

Marc Nairn

Published Feb 1, 2026• 1 citation
Publisher

Abstract

This record contains code, notebooks, data, and figure scripts associated with the project. The description below lists the contents of the upload and their roles. strongsym.zip: Zip archive used for the Zenodo upload. It contains the simul/, finsize_data/, and dependencies/ directories. simul/ : Simulation and analysis notebooks and scripts. MF_*.ipynb - Mean-field analysis notebooks. finite_size_spins.ipynb - Finite-size dynamics for 2 level system alt_finite_size_spins.ipynb - Alternative finite-size with a stress on Liouvullian spectrum analysis in the two level system. liouv_spec_spin1.ipynb -Finite-size spectrum analysis for three level model. find_symmetry.ipynb - Symbolic symmetry-finding notebook. finsize_data/ : Data files used to generate figures. fig_script.ipynb: Notebook to generate the figures overview_sketch.ipynb: Notebook to generate schmatics and diagrams (no raw data) dependencies/ : Environment specification files, for both python (.yml) and julia (.toml) Usage: Unzip strongsym.zip, set up environments using the files in dependencies/, and run the notebooks in simul/. Data required for figures is in finsize_data/; figure scripts are at the repository root. For a more detailed walkthrough and documentation, see the GitHub repository: https://github.com/MarcNairn/spin_strong_symmetries_phase

DOI
10.5281/zenodo.18017643

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This record contains code, notebooks, data, and figure scripts associated with the project. The description below lists the contents of the upload and their roles. strongsym.zip: Zip archive used for the Zenodo upload. It contains the simul/, finsize_data/, and dependencies/ directories. simul/ : Simulation and analysis notebooks and scripts. MF_*.ipynb - Mean-field analysis notebooks. finite_size_spins.ipynb - Finite-size dynamics for 2 level system alt_finite_size_spins.ipynb - Alternative finite-size with a stress on Liouvullian spectrum analysis in the two level system. liouv_spec_spin1.ipynb -Finite-size spectrum analysis for three level model. find_symmetry.ipynb - Symbolic symmetry-finding notebook. finsize_data/ : Data files used to generate figures. fig_script.ipynb: Notebook to generate the figures overview_sketch.ipynb: Notebook to generate schmatics and diagrams (no raw data) dependencies/ : Environment specification files, for both python (.yml) and julia (.toml) Usage: Unzip strongsym.zip, set up environments using the files in dependencies/, and run the notebooks in simul/. Data required for figures is in finsize_data/; figure scripts are at the repository root. For a more detailed walkthrough and documentation, see the GitHub repository: https://github.com/MarcNairn/spin_strong_symmetries_phase
This repository contains the numerical data and computational codes supporting the results presented in the manuscript: "Engineered non-Gaussian Coherence as a Thermodynamic Resource for Quantum Batteries". The archive (data_QB.zip) includes all datasets, Jupyter notebooks, and figure files required to reproduce the results reported in both the main text and the Supplemental Material. Each figure is organised in a separate directory containing the corresponding data (.txt), code (.ipynb), and generated plots (.png/.pdf). The provided materials are sufficient to reproduce all figures and results without restriction. Detailed instructions and directory structure are described in the accompanying README.txt file. This dataset is made openly available to ensure transparency and reproducibility of the reported results.
Shortcuts to adiabaticity with a quantum control field — Code & data========================================================== This repository contains a Jupyter notebook and supporting data files (csv) to reproduce the numerical results and figures in our manuscript (arXiv 2508.06304), which investigates adiabatic shortcuts mediated by a quantum control field.The code implements a Landau–Zener two-level system coupled to a spectator qubit, time evolution of the full system (LZ qubit + spectator) via QuTiP, parameter sweeps (parallelized with joblib), and plotting utilities for the manuscript figures. Related publication----------------------------This code is complementary to the preprint: https://arxiv.org/abs/2508.06304. Requirements---------------------The simulations were developed for and tested on Python 3.9+. Recommended packages and minimum versions: - Python >= 3.9- numpy >= 1.22- scipy >= 1.8- matplotlib >= 3.5- qutip >= 4.6 (install a recent stable qutip; some installations require `pip install qutip` or `conda` channels)- joblib >= 1.2- tqdm >= 4.60- pandas (optional, for CSV handling)- jupyterlab / notebook (for interactive use) Running the notebook (interactive) Open the notebook with JupyterLab or Jupyter Notebook. Execute cells sequentially in the order indicated by the notebook outline.The sections “Definitions” and “Figure imports and setup” must be executed before generating any data or figures, as they define all operators, and helper functions. The notebook is organized into separate sections for each figure of the manuscript, allowing you to re-run only the figure(s) of interest. Note: Figure 4 (Density plot) is configured to load precomputed data files to reduce computational cost. To reproduce Figure 4 without recomputing the full dataset, ensure the required .csv files are downloaded and located in the same directory as the notebook. To explore different parameter regimes: Modify the relevant parameter definitions (e.g., anneal_window, g, epsilon, omega_c, x0, x0_vals, q_vals). To include dissipation of the spectator (ancilla) qubit, set the parameter kappa> 0. For questions about reproducing the figures or about parameter choices used in the paper, contact the corresponding authors listed in the preprint. If you reuse this data/code, please cite the associated preprint and this code archive.
Underlying data and plotting for the publication 'Mid-Circuit correction of correlated phase errors using an array of spectator qubits', K. Singh, C.E. Bradley, S. Anand, V. Ramesh, R. White and H. Bernien, 2022. This directory contains: - "Data Analysis.ipynb": A jupyter notebook from which all plots presented in the main text and supplementary materials can be reproduced. - "Data.zip": A zip file containing the json files which are loaded into "Data Analysis.ipynb". These files contain x, y, and where appropriate, y error values for each plot. The notebook should be executed using python3.
This repository provides the figure data and numerical simulation codes supplement to the manuscript “Dynamical freezing and enhanced magnetometry in an interacting spin ensemble” (arXiv:2507.22982). Contents:--Main_text_figures--Extended_Data_figures Each folder contains the numerical or experimental data used to generate the corresponding figures in the manuscript. All figures can be reproduced by straightforward plotting of the included .csv files. A short description of each dataset is provided within the file headers. --Exact_diagonalization_code--Discrete_truncated_Wigner_approximation_code These directories contain the codes used in the numerical simulations presented in the paper. Each code package includes a detailed README file explaining the workflow, parameters, and usage.
Data from the simulations presented in arXiv:2104.00569, and generated with the code published in https://github.com/matteoacrossi/adapt_ic-povm # Instructions The file data.tar contains the following files: hamiltonians_h_chain.pickle hamiltonians_params.json hamiltonians.pickle chemistry.feather h_chain.feather tomography_counts_data.jsonl fidelities_tomo.json The files `hamiltonians*` contain the Hamiltonians considered in the paper, as well as the corresponding exact ground states and example VQE ground states. The files `chemistry.feather` and `h_chain.feather` contain all the results for the molecules and hydrogen chain (respectively). The files `tomography_counts_data.jsonl` and `fidelities_tomo.json` contain (respectively) the raw data for tomographical reconstruction and the fidelities of the reconstructed density matrices used to produce Fig. 5. The files can be processed using the Figures.ipynb file in the linked repository. Please refer to the README.md file for more detail on how to use and reproduce this data
Underlying data and plotting for the publication 'A dual-species Rydberg array', S. Anand, C.E. Bradley, R. White, V. Ramesh, K. Singh and H. Bernien, 2024. This directory contains:- "DataPlotting.ipynb": A jupyter notebook from which all plots presented in the main text and supplementary materials can be reproduced.- "Data.zip": A zip file containing the json files which are loaded into "DataPlotting.ipynb". These files contain x, y, and where appropriate, y error values for each plot. The notebook should be executed using python3.