  A major challenge in machine learning is the computational expense of
training these models. Model training can be viewed as a form of optimization
used to fit a machine learning model to a set of data, which can take up
significant amount of time on classical computers. Adiabatic quantum computers
have been shown to excel at solving optimization problems, and therefore, we
believe, present a promising alternative to improve machine learning training
times. In this paper, we present an adiabatic quantum computing approach for
training a linear regression model. In order to do this, we formulate the
regression problem as a quadratic unconstrained binary optimization (QUBO)
problem. We analyze our quantum approach theoretically, test it on the D-Wave
2000Q adiabatic quantum computer and compare its performance to a classical
approach that uses the Scikit-learn library in Python. Our analysis shows that
the quantum approach attains up to 2.8x speedup over the classical approach on
larger datasets, and performs at par with the classical approach on the
regression error metric.
