Figures, Data, and Code for 'Quantum-Enhanced Deterministic Inference of k-Independent Set Instances on Neutral Atom Arrays'

This is a Figshare item containing figures, data, and code for the paper 'Quantum-Enhanced Deterministic Inference of k-Independent Set Instances on Neutral Atom Arrays.'Figure 1.pdf, Figure 2.pdf, Figure 3.pdf, and Figure 4.pdf are the PDF files for figures included in the main text of the paper.c20260205_Figshare_Fig2b.m, c20260205_Figshare_Fig2c.m, c20260205_Figshare_Fig3a.m, c20260205_Figshare_Fig3b.m, and c20260205_Figshare_Fig4ab.m are MATLAB script files which can be run without external dependencies to generate the base plots included in Figures 2(b), 2(c), 3(a), 3(b), and 4(a,b), respectively.c20260205_Figshare_EET.ipynb, NumbaMIS.py, Aquila_data_directory.zip, and Fresnel_data_directory.zip are files for analyzing the DEM costs of experimental data acquired from the QuEra Aquila device and the Fresnel Pasqal device. First, unzip the two .zip files. Then, consecutively executing the cells in the .ipynb file produces the BF-DEM or Tarjan-DEM cost for inferring k-independent sets from the experimental bitstrings.c20260205_Figshare_Tarjan.ipynb and tarjan_dem_raw.csv are files for analyzing the DEM cost scaling exponent base of the Tarjan-DEM algorithm on emulated data with varying error levels. Consecutively executing the cells in the .ipynb file, or alternatively executing just the last cell, loads the emulated data points from tarjan_dem_raw.csv and produces the analysis of the exponent base with varying error levels.All scripts and notebooks were tested in standard MATLAB (R2021a) and Python scientific computing environments (python 3.9.12, matplotlib 3.9.4, networkx 3.2.1, numba 0.60.0, numpy 2.0.2, pandas 2.3.3, scipy 1.13.1). If any issues related to software versions or computational resources arise, please contact the authors.