  We develop an approach to machine learning and anomaly detection via quantum
adiabatic evolution. In the training phase we identify an optimal set of weak
classifiers, to form a single strong classifier. In the testing phase we
adiabatically evolve one or more strong classifiers on a superposition of
inputs in order to find certain anomalous elements in the classification space.
Both the training and testing phases are executed via quantum adiabatic
evolution. We apply and illustrate this approach in detail to the problem of
software verification and validation.

Quantum Physics;Machine Learning