Evolutionary quantum feature selection

Other study was realized by [5] that describes a variational quantum algorithm designed to solve unscontrained black box binary optimization problems, where the objective function Quantum Feature Selection (QFS) is a novel approach to is given as a black box. Unlike typical algorithms for optimization Feature Selection (FS) in Machine Learning (ML) that leverages where a classical objetive function is provided as a principles of Quantum Computing (QC) to enhance the Quandratic Uncontrained Binary Optimization problem and efficiency and effectiveness of traditional FS methods. The mapped toa sum of Pauli operators, this algorithm directly most informative features are typically selected in traditional handles the black box objective function. The algorithm s FS methods based on their correlation with the target variable theorical justification is based on convergence guarantees of or their predictive power. However, these methods can struggle quantum imaginary time evolution. The authors demonstrated with high-dimensional datasets, a phenomenon known as that the quantum method produced competitive, and in certain the curse of dimensionality [1]. On the other hand, Evolutionary aspects, even better perfomance compared to traditional FS Algorithms (EAs) are a family of optimization algorithms techniques used in today s industry. This suggests that quantum that are inspired by the process of natural selection and evolution.