# Completely Packed Loop Code (CPLC) — Data and Analysis Data and analysis notebooks used to produce figures in the paper **"Critical non-equilibrium phases from noisy topological memories"** ([arXiv:2601.10792](https://arxiv.org/abs/2601.10792)), by autgors Amir-Reza Negari, Subhayan Sahu, Jan Behrends, Benjamin Béri, Timothy H. Hsieh. The simulation code that generated this data is available on GitHub. https://github.com/subhayansahu/CompletelyPackedLoopsQEC ## Contents ``` CompletelyPackedLoopsQEC_data/ ├── QECPlots.ipynb # Phase diagram and coherent information figures ├── CMIPlots.ipynb # CMI/MIE figures (global, local, pairwise, Markov length) ├── SurfaceCodeFigures.ipynb # Surface code schematic figures └── data/ ├── QEC_PhaseDiagram_*.csv # 2D phase-diagram heatmaps (p, q ∈ [0,1]) ├── QEC_transition_*.csv # 1D transition cuts at q = 0.1 ├── QEC_transition_finer_*.csv # Fine-resolution transition scans ├── QEC_left_bdy_*.csv # Left-boundary coherent information ├── QEC_bottom_bdy_*.csv # Bottom-boundary coherent information ├── CoherentInfo*_fixedR.csv # Fixed-R coherent information data ├── aggregated_mu_results.csv # Aggregated mean results ├── cplc_cmi_data.npz # Processed CMI/MIE data (numpy format) └── cplc_cmi_scales/ # Raw CMI data vs system size L, # for p ∈ [0.45, 0.65], q = 0.1 ``` ## Usage The notebooks expect data files to be in the `data/` subdirectory (paths are already configured). Open with a Python kernel from the `CompletelyPackedLoopsQEC_data/` directory: ```bash jupyter notebook QECPlots.ipynb jupyter notebook CMIPlots.ipynb jupyter notebook SurfaceCodeFigures.ipynb ``` ### Dependencies - Python ≥ 3.9 - Packages: `numpy`, `matplotlib`, `scipy`, `pandas` ```bash pip install numpy matplotlib scipy pandas ``` ## Citation If you use this data or notebooks, please cite: ```bibtex @misc{negari2026criticalnonequilibriumphasesnoisy, title={Critical non-equilibrium phases from noisy topological memories}, author={Amir-Reza Negari and Subhayan Sahu and Jan Behrends and Benjamin Béri and Timothy H. Hsieh}, year={2026}, eprint={2601.10792}, archivePrefix={arXiv}, primaryClass={quant-ph}, url={https://arxiv.org/abs/2601.10792}, } ```