This Zenodo record provides an independent, self-contained reproducibility package for the figures and tables reported in the accompanying manuscript on conditional entanglement bounds and ladder exact-diagonalization (ED) benchmarks for many-body localization. Contents Google Colab notebook (MBL_entropyflow_zenodo.ipynb) that reproduces: fig_entropy_flow.png (boundary-normalized mid-spectrum entanglement vs disorder strength) fig_drift.png (finite-size drift indicator Δ(W)) summary tables exported as CSV (entanglement and drift) Archived datasets (entropy_data_new.csv, and optional quench outputs if present) used to generate the figures and tables without rerunning ED. Optional ED script (compute_data.py) for regenerating datasets from scratch (computationally heavier; intended for users who wish to reproduce raw data). What the notebook reproduces Running the notebook generates: Figures in outputs/figures/ Tables in outputs/tables/ (CSV format) including: entropy_summary.csv drift_summary.csv Computational notes The notebook is designed to run end-to-end in Google Colab. Recomputing raw ED data can be time-consuming; the included CSVs allow immediate regeneration of the paper figures/tables. Results depend on system size (e.g., 4×2 and 5×2 ladders), disorder type (random vs quasiperiodic), and sampling parameters documented in the notebook. License Choose one of the following (recommended): MIT License (best for code + data reuse), or CC BY 4.0 (best if you want strict attribution for all materials). Citation If you use this package, please cite: The Zenodo DOI for this record (automatically generated by Zenodo), And the associated manuscript/preprint (add arXiv or journal reference here once available).
This Zenodo record provides an independent, self-contained reproducibility package for the figures and tables reported in the accompanying manuscript on conditional entanglement bounds and ladder exact-diagonalization (ED) benchmarks for many-body localization. Contents Google Colab notebook (MBL_entropyflow_zenodo.ipynb) that reproduces: fig_entropy_flow.png (boundary-normalized mid-spectrum entanglement vs disorder strength) fig_drift.png (finite-size drift indicator Δ(W)) summary tables exported as CSV (entanglement and drift) Archived datasets (entropy_data_new.csv, and optional quench outputs if present) used to generate the figures and tables without rerunning ED. Optional ED script (compute_data.py) for regenerating datasets from scratch (computationally heavier; intended for users who wish to reproduce raw data). What the notebook reproduces Running the notebook generates: Figures in outputs/figures/ Tables in outputs/tables/ (CSV format) including: entropy_summary.csv drift_summary.csv Computational notes The notebook is designed to run end-to-end in Google Colab. Recomputing raw ED data can be time-consuming; the included CSVs allow immediate regeneration of the paper figures/tables. Results depend on system size (e.g., 4×2 and 5×2 ladders), disorder type (random vs quasiperiodic), and sampling parameters documented in the notebook. License Choose one of the following (recommended): MIT License (best for code + data reuse), or CC BY 4.0 (best if you want strict attribution for all materials). Citation If you use this package, please cite: The Zenodo DOI for this record (automatically generated by Zenodo), And the associated manuscript/preprint (add arXiv or journal reference here once available).
This record provides the complete figure archive and figure-generation code for the manuscript: “A Differential Null Test for Informational Corrections to Gravity-Related State Reduction.” The archive contains the final manuscript and Supplemental Material figures in both PDF and PNG formats, together with a reproducible Python/Matplotlib script for regenerating the figure set. The figures support a theoretical proposal for a differential null test of informational corrections to gravity-related quantum state reduction. The manuscript develops a matched-pair visibility-residual framework in which ordinary gravitational and environmental baseline terms are cancelled, leaving a testable bound on the informational coupling parameter. The included figures are: Fig1 — main manuscript figure showing the differential null-test structure and visibility-residual logic. FigS1–FigS5 — supplementary figures supporting the branch-density construction, perturbative regime, visibility residuals, coupling bounds, and source-to-energy schematic. The code package includes: generate_prl_figures.py — Python script for regenerating the full figure set. requirements.txt — minimal package requirements. PDF and PNG output folders. SHA256 manifest for file-integrity checking. This Zenodo record is intended to support transparency, reproducibility, and independent inspection of the visual materials associated with the manuscript. The figures are synthetic/theoretical benchmark illustrations and do not represent observational detections or experimental data. Suggested citation use: cite this record when referring to the reproducible figure-generation code or archived figure files associated with the manuscript.
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.
Overview of data provided in support of Towards near-term quantum simulation of materials Contents of this folder: `analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. `towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials. `towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript. The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, i.e. the user need not run the script to produce the output files. Users can generate these analyses directly by running ```python analyse_materials_results.py```, though note the following packages will first need to be installed: `pandas >= 1.2.3` `numpy >= 1.23` `seaborn >= 0.11.1` `lfig >= 0.1.3` `matplotlib > 3.7.0` Included data Within `towards_quantum_simulation_data`, there are subfolders for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`. Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. `hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files `interactions.json` Hamiltonian terms in terms of Majorana monomials. `map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated. `map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. `map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. `stage_data.json` contains key/value pairs of any other fields of interest. `encoding` files which specify the fermionic encoding which is customised for the material under study. `encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material. `precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`). `stage_data.json` contains key/value pairs of any other fields of interest. `algorithms` contains subfolders for each of the algorithms desribed in the manuscript Those explored for circuits depths: `TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript) `TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*) `VQESplitTerms` (VQE) `VQESplitTermsNoSwapNetwork` (VQE*) each of which contain the files, inside the `circuitry` folder: `circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit `circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit. `stage_data.json` contains key/value pairs of any other fields of interest. and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: `MeasurementCommutativity` `MeasurementNaiveQubitwise` `MeasurementNonCrossing` each of which contain the files, inside the `compilation` folder: `layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14. `stage_data.json` contains key/value pairs of any other fields of interest. CSV files In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials. `circuit_costs.csv`: results of running the circuit compiler described in the manuscript. `measurements.csv`: results of running the measurement compiler described in the manuscript. These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.
This archive contains the reproducibility package for the manuscript “Entanglement-invariant topological fingerprint transitions in high-dimensional OAM biphoton states”. The files provide deterministic Python verification scripts and output logs for the analytic and numerical claims in the paper. The package includes verify_fast.py, which reproduces the F5,3 phase table, the universal count and chirality laws, the G5,3 fixed-entropy phase structure, the mixed-block sign structure, the robustness checks, and the worked D3 discriminant calculation. It includes verify_brouwer_slow.py, which performs a direct Brouwer-degree cross-check of the affine sector-degree criterion. It also includes analyze_fingerprint.py, which reports the active-sector counts, sign-resolved counts, chirality, and entropy values for the benchmark F5,3 and G5,3 examples. The output logs verify_results.out, verify_brouwer_slow.out, and analyze_fingerprint.out record the deterministic results used to support the manuscript. No experimental datasets are included; the associated article is a theoretical and computational study.
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.