Implementation of Markov Decision Processes into quantum algorithms for reinforcement learning Pegalajar Cuéllar, Manuel Pegalajar Palomino, María del Carmen Baca Ruiz, Luis Gonzaga Navarro Garulo, Gabriel Cano Gutiérrez, Carlos Servadei, Lorenzo quantum reinforcement learning quantum machine learning In this work, we propose a methodology to implement classic Markov Decision Processes in a Quantum Computing paradigm, as a first step to achieve systems running Quantum Reinforcement Learning where both agent and environment are expressed as quantum programs. To do so, we analyze the interaction cycle between the agent and the environment in classic reinforcement learning and create a method to map a Markov Decision Process with discrete state space, action set, and rewards, into a quantum program. 2024-12-17T12:05:53Z 2024-12-17T12:05:53Z 2023-07-29 conference output Pegalajar, M.P., Pegalajar, M.C., Ruiz, L.G.B., Navarro, G., Cano, C., Servadei, L., Implementation of Markov Decision Processes into quantum algorithms for reinforcement learning, 1st European Workshop on Quantum Artificial Intelligence, Naples (Italiy), 28-29 July 2023, pp.1-3 https://hdl.handle.net/10481/98141 eng open access University of Naples Federico II