Towards Performance Benchmarking for Quantum Reinforcement Learning
| dc.contributor.author | Reers, Volker | |
| dc.contributor.editor | Klein, Maike | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Winter, Cornelia | |
| dc.contributor.editor | Wohlgemuth, Volker | |
| dc.date.accessioned | 2023-11-29T14:50:19Z | |
| dc.date.available | 2023-11-29T14:50:19Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | This research paper explores the potential benefits and challenges of using Quantum Computing to enhance Reinforcement Learning (RL), a subset of Machine Learning that involves learning through trial and error. RL algorithms aim to learn a policy that maximizes a reward signal by interacting with an environment. Quantum Computing, which uses quantum-mechanical phenomena to process information, has been applied to several models in the realm of Machine Learning (ML). It therefore has the potential to accelerate computations of ML models which may be used to improve the efficiency of RL. However, current hardware limitations of quantum computers and the difficulty of designing quantum algorithms that outperform classical algorithms are major challenges to overcome. This paper discusses different approaches to using Quantum Computing for RL, such as using quantum algorithms to solve specific subproblems. In particular, we aim towards a performance benchmark of Quantum Reinforcement Learning using an implementation of Quantum Deep Q Learning as an example. While there is still much research to be done, the potential benefits of using Quantum Computing for RL are significant and warrant further exploration as quantum computing technology continues to evolve. | en |
| dc.identifier.doi | 10.18420/inf2023_126 | |
| dc.identifier.isbn | 978-3-88579-731-9 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/43048 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | INFORMATIK 2023 - Designing Futures: Zukünfte gestalten | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-337 | |
| dc.subject | Quantum Reinforcement Learning | |
| dc.subject | Quantum Deep Q Learning | |
| dc.subject | Quantum Performance Benchmarking | |
| dc.title | Towards Performance Benchmarking for Quantum Reinforcement Learning | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 1145 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 1135 | |
| gi.conference.date | 26.-29. September 2023 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | GI Quantum Computing Workshop |
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