  Effective feature selection is essential for enhancing the performance of
artificial intelligence models. It involves identifying feature combinations
that optimize a given metric, but this is a challenging task due to the
problem's exponential time complexity. In this study, we present an innovative
heuristic called Evolutionary Quantum Feature Selection (EQFS) that employs the
Quantum Circuit Evolution (QCE) algorithm. Our approach harnesses the unique
capabilities of QCE, which utilizes shallow depth circuits to generate sparse
probability distributions. Our computational experiments demonstrate that EQFS
can identify good feature combinations with quadratic scaling in the number of
features. To evaluate EQFS's performance, we counted the number of times a
given classical model assesses the cost function for a specific metric, as a
function of the number of generations.

Quantum Physics;Machine Learning;Combinatorics