  Maximum likelihood estimation (MLE) is one of the most important methods in
machine learning, and the expectation-maximization (EM) algorithm is often used
to obtain maximum likelihood estimates. However, EM heavily depends on initial
configurations and fails to find the global optimum. On the other hand, in the
field of physics, quantum annealing (QA) was proposed as a novel optimization
approach. Motivated by QA, we propose a quantum annealing extension of EM,
which we call the deterministic quantum annealing expectation-maximization
(DQAEM) algorithm. We also discuss its advantage in terms of the path integral
formulation. Furthermore, by employing numerical simulations, we illustrate how
it works in MLE and show that DQAEM outperforms EM.
