An Introduction to Quantum Reinforcement Learning (QRL)

Fuente: arXiv
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Main Author: Chen, Samuel Yen-Chi
Format: Preprint
Published: 2024
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author Chen, Samuel Yen-Chi
author_facet Chen, Samuel Yen-Chi
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_05846
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An Introduction to Quantum Reinforcement Learning (QRL)
Chen, Samuel Yen-Chi
Quantum Physics
Artificial Intelligence
Emerging Technologies
Machine Learning
Neural and Evolutionary Computing
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.
title An Introduction to Quantum Reinforcement Learning (QRL)
topic Quantum Physics
Artificial Intelligence
Emerging Technologies
Machine Learning
Neural and Evolutionary Computing
url https://arxiv.org/abs/2409.05846