Abstract This paper introduces a novel method to improve the decision-making process of reinforcement learning agents via quantum information technology methods. In this approach, states |s〉 of the system are replaced by quantum states (eigenfunctions) of the system |ψ〉, in which a system can be in a superposition of states, and rewards of each step are calculated based on the calculated eigenvalues of the previous step. The agent decides the next step of the system based on the result of the quantum gate’s effect on the available options. Due to the high-speed, escalated performance of quantum algorithms, this method will improve the performance of reinforcement learning agents in unknown environments.
Abstract This paper introduces a novel method to improve the decision-making process of reinforcement learning agents via quantum information technology methods. In this approach, states |s〉 of the system are replaced by quantum states (eigenfunctions) of the system |ψ〉, in which a system can be in a superposition of states, and rewards of each step are calculated based on the calculated eigenvalues of the previous step. The agent decides the next step of the system based on the result of the quantum gate’s effect on the available options. Due to the high-speed, escalated performance of quantum algorithms, this method will improve the performance of reinforcement learning agents in unknown environments.
The current natural evolution of the world of real-time decision-making algorithms is unable to keep up with the fast changing complexity and dynamics of today’s world. Particularly in automotive, smart grids, robotics, or other applying fields, such ultra efficient decision making is demanded in a situation of rapidly changing conditions with high requirements on computation resources. As the first contribution, Quantum Inspired Reinforcement Learning (QIRLF) proposes a new method of addressing these challenges via marrying the fundamental elements of quantum theory to reinforcement learning. At the same time, QIRLF exploits quantum inspired state representation to explore many potential states concomitantly, thereby improving the efficiency in its decisions making process. Besides quantum tunneling and probabilistic action choice mechanisms faster convergence to optimal solutions are possible in values of state spaces, and multi-agent entanglement improves cooperation between the agents. The framework is adaptable in being able to make real time decisions in dynamic environments and is efficient by having quantum feedback loops that continuously refine decision policies. QIRLF reduces significantly the decision-making time and computational load compared to traditional, by using quantum inspired techniques. By applying this approach we not only enhance decision speed and reliability but also help in developing effective, energy efficient solutions for the complex real time systems, making it possible for more advanced, environment friendly technologies in systems needing fast, accurate and quick decisions.
This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov decision process. By employing quantum concepts and a quantum search algorithm, this work presents the implementation and optimization of the agent-environment interactions entirely within the quantum domain, eliminating reliance on classical computations. Key contributions include the quantum-based state transitions, return calculation, and trajectory search mechanism that utilize quantum principles to demonstrate the realization of RL processes through quantum phenomena. The implementation emphasizes the fundamental role of quantum superposition in enhancing computational efficiency for RL tasks. Results demonstrate the capacity of a quantum model to achieve quantum enhancement in RL, highlighting the potential of fully quantum implementations in decision-making tasks. This work not only underscores the applicability of quantum computing in machine learning but also contributes to the field of quantum reinforcement learning by offering a robust framework for understanding and exploiting quantum computing in RL systems.
This paper proposes a novel action selection method based on quantum computation and reinforcement learning (RL). Inspired by the advantages of quantum computation, the state/action in a RL system is represented with quantum superposition state. The probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. And the action selection is carried out by observing quantum state according to collapse postulate of quantum measurement. The results of simulated experiments show that quantum computation can be effectively used to action selection and decision making through speeding up learning. This method also makes a good tradeoff between exploration and exploitation for RL using probability characteristics of quantum theory.
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.
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.
This paper investigates the application of quantum-inspired algorithms to the field of reinforcement learning (RL). Traditional RL methods often struggle with exploration challenges and can be computationally expensive, particularly in complex environments. We propose a novel RL algorithm that leverages concepts from quantum mechanics, specifically superposition and entanglement, to address these limitations. The core idea is to represent states, actions, and rewards using quantum-inspired representations, allowing the agent to explore the environment more efficiently and potentially converge to optimal policies faster. This work presents a detailed formulation of the algorithm, including the mathematical framework and key considerations for implementation. The potential benefits of this approach, including improved exploration and reduced computational complexity, are discussed. The algorithm is designed to iteratively refine its policy based on simulated quantum measurements, aiming to minimize the expected return. The exploration strategy is guided by the inherent probabilistic nature of quantum states, leading to a more diverse and potentially faster learning process. Further research is warranted to fully explore the capabilities and limitations of this approach across various RL benchmark problems.