Deterministic Quantum Annealing Expectation-Maximization Algorithm

Abstract

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.

References in corpus (3)

• An introduction to quantum machine learning
• Mathematical Foundation of Quantum Annealing
• An introduction to quantum annealing

Cited by in corpus (5)

• Quantum Expectation-Maximization Algorithm
• A Quantum Extension of Variational Bayes Inference
• Quantum natural gradient without monotonicity
• A comparative study of methods to estimate conversion gain in sub-electron and multi-electron read noise regimes
• Information geometry of nonmonotonic quantum natural gradient