Quantum Reinforcement Learning via Policy Iteration

Reinforcement learning has had a great impact in decision making problems, in particular combined with artificial neural networks [1, 2]. Nevertheless, alternatives to neural networks are still needed for a number of different reasons, first, because the amount of data is expected to continue to grow along with its dimensionality, and, second, neural networks carry vulnerabilities that make them prone to adversarial attacks [3]. A possible alternative to deep learning for further improving machine learning can be found in quantum computing that has shown to be able to perform tasks beyond the reach of classical computing [4]. The field of quantum machine learning explores how to design and implement quantum algorithms that could enable machine learning that is faster, more expressive, or more explainable. Using quantum computers, a number of quantum machine learning algorithms have been published for supervised and unsupervised learning [5-11].