  The problem of selecting an appropriate number of features in supervised
learning problems is investigated in this paper. Starting with common methods
in machine learning, we treat the feature selection task as a quadratic
unconstrained optimization problem (QUBO), which can be tackled with classical
numerical methods as well as within a quantum computing framework. We compare
the different results in small-sized problem setups. According to the results
of our study, whether the QUBO method outperforms other feature selection
methods depends on the data set. In an extension to a larger data set with 27
features, we compare the convergence behavior of the QUBO methods via quantum
computing with classical stochastic optimization methods. Due to persisting
error rates, the classical stochastic optimization methods are still superior.

Quantum Physics;Computational Finance