On the Use of Quantum Reinforcement Learning in Energy-Efficiency Scenarios

Eva Andrés, Manuel Pegalajar Cuéllar () and Gabriel Navarro
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Eva Andrés: Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain
Manuel Pegalajar Cuéllar: Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain
Gabriel Navarro: Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain

Energies, 2022, vol. 15, issue 16, 1-24

Abstract: In the last few years, deep reinforcement learning has been proposed as a method to perform online learning in energy-efficiency scenarios such as HVAC control, electric car energy management, or building energy management, just to mention a few. On the other hand, quantum machine learning was born during the last decade to extend classic machine learning to a quantum level. In this work, we propose to study the benefits and limitations of quantum reinforcement learning to solve energy-efficiency scenarios. As a testbed, we use existing energy-efficiency-based reinforcement learning simulators and compare classic algorithms with the quantum proposal. Results in HVAC control, electric vehicle fuel consumption, and profit optimization of electrical charging stations applications suggest that quantum neural networks are able to solve problems in reinforcement learning scenarios with better accuracy than their classical counterpart, obtaining a better cumulative reward with fewer parameters to be learned.

Keywords: quantum neural networks; variational quantum circuits; quantum reinforcement learning; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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