Quantum Annealing based Feature Selection

Feature selection is crucial for enhancing the accuracy and efficiency of machine learning models. Calculating the optimal feature set for maximum mutual information (MI) and conditional mutual information (CMI) remains computationally intractable for large datasets on classical computers, even with approximation methods. This study employs a Mutual Information Quadratic Unconstrained Binary Optimization (MIQUBO) formulation, enabling its solution on a quantum annealer. To showcase its real-world applicability, we apply MIQUBO to forecasting the price of used excavators. Our results indicate that using the MIQUBO approach there might be an improvement in the prediction of machine learning models for datasets, with a smaller MI concentration.