  Quantum information processing is likely to have far-reaching impact in the
field of artificial intelligence. While the race to build an error-corrected
quantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devices
provide an immediate platform for exploring a possible quantum advantage
through hybrid quantum--classical machine learning algorithms. One example of
such a hybrid algorithm is "quantum kitchen sinks", which builds upon the
classical algorithm known as "random kitchen sinks" to leverage a gate model
quantum computer for machine learning applications. We propose an alternative
algorithm called "adiabatic quantum kitchen sinks", which employs an adiabatic
quantum device to transform data features into new features in a non-linear
manner, which can then be employed by classical machine learning algorithms. We
present the effectiveness of our algorithm for performing binary classification
on both a synthetic dataset and a real-world dataset. In terms of
classification accuracy, our algorithm significantly enhances the performance
of a classical linear classifier on the studied binary classification tasks and
can potentially be implemented on a current adiabatic quantum device to solve
practical problems.
