An Introduction to Quantum Reinforcement Learning (QRL)

Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broader AI and ML community.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.1109/ictc62082.2024.10827651
Citations
9
Primary Topic
Quantum Computing Algorithms and Architecture
Type
article
Field-Weighted Citation Impact
3.13
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article

An Introduction to Quantum Reinforcement Learning (QRL)

Samuel Yen-Chi Chen
9 citations
Quantum Computing Algorithms and Architecture
3.13
article

An Introduction to Quantum Reinforcement Learning (QRL)

Samuel Yen-Chi Chen
article en
9 citations

Abstract

Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broader AI and ML community.

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Openalex Percentile: Top 7%
Quantum Computing Algorithms and Architecture
3.13
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