Implementation of Markov Decision Processes into quantum algorithms for reinforcement learning

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URI: https://hdl.handle.net/10481/98141
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Autor
Pegalajar Cuéllar, Manuel; Pegalajar Palomino, María del Carmen; Baca Ruiz, Luis Gonzaga; Navarro Garulo, Gabriel; Cano Gutiérrez, Carlos; Servadei, Lorenzo
Editorial
University of Naples Federico II
Materia
quantum reinforcement learning
 
quantum machine learning
 
Fecha
2023-07-29
Referencia bibliográfica
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
Patrocinador
This article was supported by the project QUANERGY (Ref. TED2021-129360B-I00), Ecological and Digital Transition R&D projects call 2022 funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR.
Resumen
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.
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