Quantum-Enhanced Reinforcement Learning (QRL) for Robotics

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.

DOI: 10.5281/zenodo.22281086

Related papers

Quantum-Enhanced Reinforcement Learning for Robotics

Jincheng Zhang

Quantum-enhanced reinforcement learning (QEL) offers the potential to significantly improve the performance of robotic systems, particularly in navigating complex environments and adapting to dynamic situations. This paper explores the integration of quantum computing with reinforcement learning to accelerate learning, enhance exploration, and improve adaptation. We propose a novel approach that leverages quantum annealing and quantum reinforcement learning to overcome the limitations of classical RL in challenging scenarios. The core claim is that combining these two paradigms will lead to enhanced robot navigation and control capabilities. The study focuses on demonstrating the effectiveness of this combined approach through simulations and preliminary results.

Sep 7, 20260 citations

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

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Shruti Doiphode, Prof. Mukul Jagtap

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation

International Journal for Research in Applied Science and Engineering TechnologyOct 18, 20240 citations

Path Planning for Autonomous Robots using Quantum Reinforcement Learning

L. Tharun, .Swetha Shailaja, G. Aswitha +1 more

ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTFeb 26, 20260 citations

Application of Quantum Reinforcement Learning in Autonomous Unmanned Aerial Vehicle Navigation

Veljko Perić, Milan Živković

An intelligent adaptation technique is necessary due to the complexity and unpredictability of the air space, and autonomous unmanned aerial vehicle (UAV) navigation uses quantum reinforcement learning (QRL) as an advanced control mechanism. This work suggests quantum-inspired encoding and optimization algorithms for policy learning in order to overcome the shortcomings in efficiency and scalability of conventional and deep reinforcement learning approaches. The technique employs quantum-parametric policy optimization on a hybrid quantum-classical architecture after forming UAV navigation as a Markov Decision Process and adding quantum amplitude encoding. Dynamic obstacle and random wind disturbance experiments will be carried out in a high-fidelity 3D simulation environment. QRL has decreased energy usage by up to 17% and increased the top classical RL baseline's convergence speed by 40%. Even in inclement weather, the success rate remains around 95%, and the minimum obstacle clearance never falls below 1.8 meters. Analysis shows that QRL yields safer, more robust and sample-efficient policies that are better at generalizing to unseen maps and disturbance-heavy environments. Based on the above results, the technical feasibility and engineering value of quantum-enhanced learning algorithms for robust UAV autonomous navigation have been demonstrated; thus, they can serve as a model for future intelligent aerial decision systems.

Journal of Applied Automation TechnologiesDec 13, 20250 citations

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