The data repository contains data sets obtained with Quantum-approximate optimization algorithm (QAOA) simulations and experiments for the main article in [1]. For a comprehensive understanding, please check readme_qaoa_mvsic.pdf file and refer to the main article in [1]. We design, theoretically model, simulate and experiment QAOA-MVSIC algorithm combining QAOA, majority voting (MV) and successive interference cancellation (SIC) to target experimental challenges of QAOA for maximum-likelihood (ML) decoding for n × n massive multi-input multi-output (MIMO) systems with large n.
The data repository includes simulation parameters and results for the Quantum Approximate Optimization Algorithm (QAOA) channel decoding applied to short block-lengths in Additive White Gaussian Noise (AWGN) channels. This research utilizes a Random Linear Code (RLC) in a Coded Modulation (CM) design at the encoder. Extensive simulations were conducted using IBM Quantum Lab and the ibmq_qasm_simulator back-end provided by IBM Quantum, simulating quantum circuits on classical hardware.
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.