Automatic evolutionary design of Quantum Rule-Based Systems and applications to Quantum Reinforcement Learning Pegalajar Cuéllar, Manuel Pegalajar Jiménez, María Del Carmen Cano Gutiérrez, Carlos Quantum Rule-Based System Quantum Reinforcement Learning Quantum Artificial Intelligence Explainable Artificial Intelligence Evolutionary algorithms Funding for open access publishing: Universidad de Granada/CBUA. This article was funded by the project QUANERGY (Ref. TED2021-129360B-I00), Ecological and Digital Transition R &D projects call 2022 by MCIN/AEI/10.13039/501100011033 and European Union NextGeneration EU/PRTR. Explainable Artificial Intelligence is a research topic whose relevance has increased in recent years, especially with the advent of large machine learning models. However, very few attempts have been proposed to improve interpretabil- ity in the case of Quantum Artificial Intelligence, and many existing Quantum Machine Learning models in the literature can be considered almost as black boxes. In this article, we argue that an appropriate semantic interpretation of a given quantum circuit that solves a problem can be of interest to the user not only to certify the correct behavior of the learned model, but also to obtain a deeper insight into the problem at hand and its solution. We focus on decision-making problems that can be formulated as classification tasks and propose a method for learning Quantum Rule-Based Systems to solve them using evolutionary optimization algorithms. The approach is tested to learn rules that solve control and decision-making tasks in reinforcement learning environments, to provide interpretable agent policies that help to understand the internal dynamics of an unknown environment. Our results conclude that the learned policies are not only highly explainable, but can also help detect non-relevant features of problems and produce a minimal set of rules. 2024-06-10T11:09:40Z 2024-06-10T11:09:40Z 2024-05-06 journal article Published version: Cuéllar, M.P., Pegalajar, M.C. & Cano, C. Automatic evolutionary design of quantum rule-based systems and applications to quantum reinforcement learning. Quantum Inf Process 23, 179 (2024). https://doi.org/10.1007/s11128-024-04391-0 https://hdl.handle.net/10481/92462 10.1007/s11128-024-04391-0 eng info:eu-repo/grantAgreement/EC/NextGenerationEU/TED2021-129360B-I00 open access Springer Nature