  We present a real-world application that uses a quantum computer.
Specifically, we train a RBM using QA for cybersecurity applications. The
D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data,
which is a benchmark dataset for cybersecurity. For comparison, RBMs are also
trained using CD. CD is a commonly used method for RBM training. Our analysis
of the ISCX data shows that the dataset is imbalanced. We present two different
schemes to balance the training dataset before feeding it to a classifier. The
first scheme is based on the undersampling of benign instances. The imbalanced
training dataset is divided into five sub-datasets that are trained separately.
A majority voting is then performed to get the result. Our results show the
majority vote increases the classification accuracy up from 90.24% to 95.68%,
in the case of CD. For the case of QA, the classification accuracy increases
from 74.14% to 80.04%. In the second scheme, a RBM is used to generate
synthetic data to balance the training dataset. We show that both QA and
CD-trained RBM can be used to generate useful synthetic data. Balanced training
data is used to evaluate several classifiers. Among the classifiers
investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better
than other classifiers. They both show an accuracy of 93%. Our results show a
proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset.
The illustrative example suggests the possibility to migrate many practical
classification problems to QA-based techniques. Further, we show that synthetic
data generated from a RBM can be used to balance the original dataset.
