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

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.