Maximum-Likelihood Detection with QAOA for Massive MIMO and Sherrington-Kirkpatrick Model with Local Field at Infinite Size

The data repository contains detailed information about  theoretical model used in the simulations and data sets obtained with simulations for the article with the title "Maximum-Likelihood Detection with QAOA for Massive MIMO and Sherrington-Kirkpatrick Model  with Local Field at Infinite Size". For a comprehensive understanding, please refer to the main article.  We apply Quantum-approximate optimization algorithm (QAOA) on maximum-likelihood (ML) detection of massive multiple-input multiple output (MIMO) systems.  We provide extensive simulation studies for QAOA by analyzing statistical properties of QAOA measurements in IBM Quantum Lab. We share corresponding measurement statistics for the costs  and total number of bit errors for a total of 236500 individual QAOA circuits for varying system size n, QAOA circuit depth p and  signal-to-noise (SNR). Researchers can access the dataset and its associated documentation for further analysis and verification.