This work presents a detailed survey of Quantum Reinforcement Learning (QRL), describing its fundamental principles and applications. By illustrating several research works, we provide the advantages and disadvantages of different QRL approaches, offering efficient insights for researchers in the field. To guide researchers in selecting the best QRL approach that meets their intended tasks, we propose a method based on the type of environment as either classical or quantum. Additionally, the paper outlines future research directions, focusing on utilizing QRL to optimize Quantum Sensor Circuits (QSCs) in various quantum physics applications. The proposed survey enhances the understanding and utilization of QRL, paving the way for more efficient developments in the field.
This work presents a detailed survey of Quantum Reinforcement Learning (QRL), describing its fundamental principles and applications. By illustrating several research works, we provide the advantages and disadvantages of different QRL approaches, offering efficient insights for researchers in the field. To guide researchers in selecting the best QRL approach that meets their intended tasks, we propose a method based on the type of environment as either classical or quantum. Additionally, the paper outlines future research directions, focusing on utilizing QRL to optimize Quantum Sensor Circuits (QSCs) in various quantum physics applications. The proposed survey enhances the understanding and utilization of QRL, paving the way for more efficient developments in the field.
Quantum Reinforcement Learning (QRL) has emerged as a promising research field, leveraging the principles of quantum mechanics to enhance the performance of reinforcement learning (RL) algorithms. However, despite its growing interest, QRL still faces significant challenges. It is still uncertain if QRL can show any advantage over classical RL beyond artificial problem formulations. Additionally, it is not yet clear which streams of QRL research show the greatest potential. The lack of a unified benchmark and the need to evaluate the reliance on quantum principles of QRL approaches are pressing questions. This work aims to address these challenges by providing a comprehensive comparison of three major QRL classes: Parameterized Quantum Circuit based QRL (PQC-QRL) (with one policy gradient (QPG) and one Q-Learning (QDQN) algorithm), Free Energy based QRL (FE-QRL), and Amplitude Amplification based QRL (AA-QRL). We introduce a set of metrics to evaluate the QRL algorithms on the widely applicable benchmark of gridworld games. Our results provide a detailed analysis of the strengths and weaknesses of the QRL classes, shedding light on the role of quantum principles in QRL and paving the way for future research in this field.
Recent research in quantum computing has gained significant momentum, promising transformative advances across various fields, including artificial intelligence (AI). This paper focuses on quantum reinforcement learning (QRL), which utilizes quantum neural network (QNN) to address the inherent chal-lenges in conventional reinforcement learning (RL) frameworks. For a comprehensive examination of QRL, in this paper, the structure of QNN is explored in-depth, including the functional roles of key quantum circuit gates such as the unitary, rotation, controlled-X, and Hadamard gates. This paper also discusses the primary components of QNN, namely state encoding, parameterized quantum circuits (PQC), and measurement processes. Furthermore, the application of IBM qiskit for the visualization of quantum states and circuits is highlighted, providing practical insights into the deployment of quantum principles in RL. Consequently, this paper encapsulates the potential of QRL to revolutionize AI, positioning it as a pivotal area of future research and development in quantum computing.
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.
Quantum reinforcement learning is an emerging field at the intersection of quantum computing and machine learning. While we intend to provide a broad overview of the literature on quantum reinforcement learning - our interpretation of this term will be clarified below - we put particular emphasis on recent developments. With a focus on already available noisy intermediate-scale quantum devices, these include variational quantum circuits acting as function approximators in an otherwise classical reinforcement learning setting. In addition, we survey quantum reinforcement learning algorithms based on future fault-tolerant hardware, some of which come with a provable quantum advantage. We provide both a birds-eye-view of the field, as well as summaries and reviews for selected parts of the literature.
Quantum Reinforcement Learning (QRL) represents a groundbreaking approach at the intersection of quantum computing and artificial intelligence, promising to revolutionize decision-making processes across various domains. This chapter explores the fundamental principles of QRL and its diverse applications in critical sectors, including transportation networks, healthcare, financial decision-making, and smart energy systems. By leveraging the computational advantages of quantum algorithms, QRL enhances traditional reinforcement learning techniques, enabling more efficient data processing and improved strategic decision-making capabilities. Case studies within this chapter illustrate the transformative potential of QRL in optimizing logistics, improving medical imaging, and enhancing energy management strategies. The findings underscore the need for further exploration of quantum applications to fully harness their capabilities and address contemporary challenges. This comprehensive examination provides a solid foundation for researchers and practitioners aiming to integrate quantum technologies into real-world applications, paving the way for innovative solutions and sustainable advancements.
This paper presents a comprehensive survey of Quantum Multi-Agent Reinforcement Learning (QMARL), a nascent field at the intersection of quantum computing and multi-agent systems. The survey begins by introducing the fundamentals of quantum computing, highlighting its potential to revolutionize computational capabilities. We then delve into the principles of multi-agent reinforcement learning (MARL), examining how quantum computing can enhance learning efficiency and decision-making processes in complex environments. The core of the survey focuses on the current state of QMARL, reviewing existing literature, methodologies, and case studies that demonstrate the integration of quantum algorithms with MARL frameworks. The paper also addresses the unique challenges and opportunities presented by quantum technologies in multi-agent systems, such as quantum entanglement and superposition, and their implications for agent coordination and learning dynamics. Additionally, the survey explores the practical applications of QMARL in various domains, including cybersecurity, finance, and robotics, underscoring its transformative potential. The paper concludes by identifying key research gaps and proposing future directions for the development of QMARL. This includes the need for scalable quantum algorithms, the exploration of quantum-resistant strategies in adversarial settings, and the integration of quantum principles in agent communication and collaboration. Overall, this survey serves as a foundational guide for researchers and practitioners interested in the emerging field of QMARL, offering insights into its current achievements and future possibilities.