Chinese Control Conference (CCC)

Complexity analysis of Quantum reinforcement learning

Chunlin Chen, Daoyi Dong

Published Jul 29, 2010• 0 citations

Abstract

Quantum reinforcement learning has been systematically presented in a recent paper [Dong et al, Quantum reinforcement learning, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, Vol. 38, No. 5, pp.1207–1220, 2008], and such results have shown that quantum reinforcement learning is an effective approach for the solutions to some complex problems. The purpose of this paper is to analyze the complexity of quantum reinforcement learning. In particular, storage complexity and exploration complexity are defined and a collection of results are presented to demonstrate such complexities by several simple examples.

Related papers

Quantum Reinforcement Learning Algorithm

Jincheng Zhang

Quantum Reinforcement Learning (QRL) represents a novel approach to reinforcement learning that leverages the unique capabilities of quantum computing to enhance learning efficiency and enable the exploration of complex behavioral rules. This paper explores the theoretical foundations, design principles, and potential applications of a QRL algorithm, focusing on its ability to learn intricate patterns and make superior decisions within challenging environments. The core mechanism centers around quantum superposition and entanglement, strategically employed to represent the state space and guide the agent's exploration, resulting in improved learning speed and robustness. We present a detailed framework for implementing this algorithm, discussing its potential impact on diverse reinforcement learning scenarios, particularly those encountered in edge applications. The paper concludes with a discussion of ongoing research directions and future prospects for QRL.

Sep 3, 20260 citations

An Introduction to Quantum Reinforcement Learning (QRL)

Samuel Yen-Chi Chen

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.

arXiv:2409.05846International Conference on Information and Communication Technology Convergence (ICTC)Oct 16, 202414 citations

Quantum Reinforcement Learning: Principles and Research Progress

Yuxuan Zhao, Kuntuo Zhu

Quantum reinforcement learning (QRL) is a burgeoning area that lies at the convergence of quantum computing and machine learning. Based on the characteristics of quantum superposition and quantum entanglement, quantum reinforcement learning can achieve some quantum advantages on existing noisy intermediate-scale quantum devices compared to traditional reinforcement learning. Within the field of information systems, reinforcement learning techniques have been extensively explored for applications including intelligent decision support mechanisms, resource distribution strategies, and system enhancement under conditions of uncertainty. This paper analyzes the fundamental principles of quantum reinforcement learning and introduces research work and progress in related areas. This study examines the foundational concepts underlying quantum reinforcement learning and provides an overview of notable algorithmic strategies documented in contemporary research.

International Conference on Innovation Management and Information Systems (ICIMIS)Mar 27, 20260 citations

Quantum Reinforcement Learning Applied to Board Games

Miguel Teixeira, Ana Paula Rocha, António J. M. Castro

Reinforcement learning is a machine learning paradigm where an agent learns how to optimize its behavior solely through its interaction with the environment. It has been extensively studied and successfully applied to complex problems of many different domains in the past decades, i.e., robotics, games, scheduling. However, the performance of these algorithms becomes limited as the complexity and dimension of the state-action space increases. Recent advances in quantum computing and quantum information have sparked interest in possible applications to machine learning. By taking advantage of quantum mechanics, it is possible to efficiently process immense quantities of information and improve computational speed. In this work, we combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. From the results, we concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space.

IEEE/WIC/ACM International Conference on Web IntelligenceDec 14, 20211 citation

Optimization of Reinforcement Learning Using Quantum Computation

Roopa Ravish, Nischal R. Bhat, N. Nandakumar +3 more

Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL’s potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.

IEEE AccessJan 1, 202410 citations

Quantum Machine Learning for Robotics: A Comprehensive Review of Algorithms, Applications, and Future Directions

Hassen Nigatu, Gaokun Shi, Jituo Li +2 more

The convergence of quantum computing and robotics represents a paradigm shift in autonomous systems design, promising to overcome fundamental computational limitations that have constrained classical robotic capabilities. This comprehensive review surveys the rapidly evolving field of quantum machine learning (QML) for robotics, systematically analyzing 107 peer-reviewed contributions spanning quantum algorithms, machine learning techniques, and their robotic applications. We provide a detailed historical perspective on the field's development, from the early theoretical foundations established by Grover's search algorithm and quantum reinforcement learning proposals to current experimental implementations on noisy intermediate-scale quantum (NISQ) devices. The review systematically examines five major application domains: (1) quantum reinforcement learning for navigation and control, (2) quantum search and path planning algorithms, (3) quantum-enhanced localization and mapping, (4) quantum optimization for inverse kinematics and task allocation, and (5) multi-robot coordination and swarm intelligence. For each domain, we present detailed algorithmic formulations, computational complexity analyses, and critical assessments of experimental results. Our analysis reveals that while provable quantum advantage in robotics remains largely prospective, recent demonstrations show promising results: quantum deep reinforcement learning achieving 40\% parameter reduction in navigation tasks, Grover-based kinematic optimization demonstrating up to 93x speedups, and quantum annealing providing practical solutions for multi-robot routing. We identify key technical challenges, including barren plateaus in variational circuits, noise-induced performance degradation, and the quantum-classical integration bottleneck. Finally, we outline a strategic roadmap for quantum robotics research, distinguishing near-term opportunities in hybrid algorithms from long-term goals requiring fault-tolerant quantum computation. This paper serves as both an authoritative technical reference and a comprehensive guide for researchers working at the intersection of quantum computing and autonomous systems.

Jun 30, 20260 citations