  Dimensionality reduction is the fundamental problem for machine learning and
pattern recognition. During data preprocessing, the feature selection is often
demanded to reduce the computational complexity. The problem of feature
selection is categorized as a NP optimization problem. Exhaustive search of
huge set of features takes huge amount of time on classical computer. In the
present paper we discuss the role of quantum adiabatic computation to perform
feature selection with bi-quadratic optimization and provide a quantum feature
selection algorithm. Our algorithm runs with the quantum adiabatic time
complexity bound $O(1/g_{min}^{2})$, which is better than classical approach
for bi-quadratic feature selection.
