Quantum Algebraic Machine Learning
This paper is considering a person identification problem based on an image of his face using the ensemble learning method and approaches to the optimization problems in the context of a training of machine learning models.
Abstract
Quantum information processing is a rapidly growing industry with promising results in the near future. Its implementtation has serious impact on almost any areas of our lives: cyber-security, communication, AI, etc. Any computations can be considered as quantum ones and in this context classical computations are a subset of quantum computations. One of the most promising applications of quantum technologies is the AI, in particular, machine learning. The intriguing question in quantum machine learning is whether there are problems in machine learning and AI that fit quantum computers naturally, but are challenging on classical hardware. In this paper we research discrete optimization problem and ensemble learning in the context of pattern recognition problems. In particular, we are considering a person identification problem based on an image of his face using the ensemble learning method and approaches to the optimization problems in the context of a training of machine learning models.