Deterministic Quantum Annealing Expectation-Maximization Algorithm
This addresses a bottleneck in machine learning optimization for researchers and practitioners using EM, though it appears incremental as it extends an existing method with quantum annealing concepts.
The authors tackled the problem of EM's sensitivity to initial configurations and inability to find global optima in maximum likelihood estimation by proposing a quantum annealing extension called DQAEM, showing through numerical simulations that it outperforms EM.
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.