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IET Quantum Communication

Design and analysis of parallel quantum transfer fractal priority replay with dynamic memory algorithm in quantum reinforcement learning for robotics

R. Palanivel, P Muthulakshmi

Published Sep 27, 2024• 2 citations
Publisher

Abstract

Abstract This paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P‐QTFPR‐DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision‐making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P‐QTFPR‐DM optimises Q ‐value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high‐dimensional state‐action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q ‐values. The autonomous vehicle, equipped with GPS for real‐time navigation, uses P‐QTFPR‐DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P‐QTFPR‐DM demonstrates a 30% reduction in decision‐making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN‐based approach achieves a 95% success rate in reaching the destination within a 5‐m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P‐QTFPR‐DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real‐world applications to enhance autonomous vehicle performance.

DOI
10.1049/qtc2.12111

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Abstract This paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P‐QTFPR‐DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision‐making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P‐QTFPR‐DM optimises Q ‐value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high‐dimensional state‐action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q ‐values. The autonomous vehicle, equipped with GPS for real‐time navigation, uses P‐QTFPR‐DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P‐QTFPR‐DM demonstrates a 30% reduction in decision‐making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN‐based approach achieves a 95% success rate in reaching the destination within a 5‐m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P‐QTFPR‐DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real‐world applications to enhance autonomous vehicle performance.
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
Reinforcement learning (RL) encounters significant challenges related to scalability and computational efficiency, particularly in complex decision-making environments. Traditional RL algorithms often struggle with large-scale tasks, necessitating innovative approaches to enhance adaptability and performance. The quantum circuit-based priority replay (QCPR) algorithm was introduced in this study, leveraging quantum-inspired techniques to enhance learning efficiency and decision-making quality through quantum computing (QC). The QCPR algorithm implements two key mechanisms for prioritizing experiences: the magnitude and direction (MaDi) based priority, which assesses the significance and directionality of experiences, and QCPR-specific methods that ensure efficient experience replay. These mechanisms are seamlessly integrated into the Q-learning (QL) framework to optimize the RL process for superior performance. QCPR was evaluated against four well-established RL algorithms-QL, deep Q-networks (DQN), prioritized experience replay (PER), and dueling DQN (DDQN)-using standard decision-making benchmarks. The algorithm was further tested in various simulation and real-world environments, including Atari games, Qiskit integration, and Raspberry Pi hardware and software setups. These evaluations demonstrated QCPR’s adaptability and robustness, showcasing its capability for dynamic, large-scale applications. The results revealed that QCPR significantly outperforms its counterparts across key performance metrics, including accuracy, precision, recall, F1-score, and cumulative rewards. Specifically, QCPR achieved a 15.29% improvement in accuracy over QL, a 9.98% increase over DQN, a 6.33% improvement over PER, and an 8.23% enhancement over DDQN. This study highlights the potential of quantum-inspired approaches to advance RL, offering scalable and efficient solutions for complex decision-making tasks.
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.
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.
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.
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.