Connected autonomous vehicles (CAVs) require extensive data processing to make real-time decisions such as obstacle detection, collision avoidance, and route optimization. To enable fast and accurate decision-making in CAVs, services traditionally executed in cloud data centers are being moved to the edge to minimize data transfer from vehicles to the cloud. We present a framework called Q-Edge that utilizes quantum computing at the network edge to improve the speed and effectiveness of data analysis for CAVs. By integrating quantum computing principles such as superposition, entanglement, and teleportation with edge computing, we propose a solution to address the latency and bandwidth issues inherent in traditional cloud computing methods. This approach, leveraging quantum software engineering practices, enables realtime decision-making, optimizing CAV performance and enhancing traffic management, urban mobility, and road safety.
Connected autonomous vehicles (CAVs) require extensive data processing to make real-time decisions such as obstacle detection, collision avoidance, and route optimization. To enable fast and accurate decision-making in CAVs, services traditionally executed in cloud data centers are being moved to the edge to minimize data transfer from vehicles to the cloud. We present a framework called Q-Edge that utilizes quantum computing at the network edge to improve the speed and effectiveness of data analysis for CAVs. By integrating quantum computing principles such as superposition, entanglement, and teleportation with edge computing, we propose a solution to address the latency and bandwidth issues inherent in traditional cloud computing methods. This approach, leveraging quantum software engineering practices, enables realtime decision-making, optimizing CAV performance and enhancing traffic management, urban mobility, and road safety.
Integrating quantum computing with edge computing presents an approach to managing the vast data generated by Connected Autonomous Vehicles (CAVs). This article introduces a novel framework that leverages quantum algorithms at the network edge to significantly enhance the efficiency and speed of data analysis for CAVs. By combining the principles of quantum computing with the distributed nature of edge computing, we propose a solution that not only addresses the latency and bandwidth challenges of conventional cloud computing models but also paves the way for real-time decision-making processes essential for autonomous vehicle operations. Our findings reveal the potential for quantum edge computing to transform the processing capabilities at the edge, offering a scalable, efficient, and faster method for data analysis that could significantly improve traffic management, urban mobility, and overall safety on the roads.
The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing's ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.
The growing complexity and volume of traffic and sensory data in Connected and Autonomous Vehicles (CAVs) make it crucial to develop more effective methods for selecting and extracting features from high-dimensional images and videos. This paper proposes a novel quantum-accelerated feature selection framework utilising the Variational Quantum Eigensolver (VQE) to enhance perception tasks in CAV environments. By decomposing high-dimensional traffic images into compact patches encoded as quantum Hamiltonians, the proposed design aims to support Intelligent Transportation Systems (ITS) tasks such as object detection, vehicle classification, and traffic monitoring in CAVs.The quantum hybrid method serves as a pipeline for extracting selective features that have been optimised using gradient-aware parameter pruning and conditional principal component analysis (PCA). The implementation of this quantum hybrid approach facilitates addressing computational challenges at the edge in embedded vehicular systems, reducing data dimensionality while preserving key discriminative spatial patterns necessary for object detection and traffic monitoring. Although the quantum method currently underperforms in certain clustering and separability metrics, it demonstrates potential as a viable alternative for advanced perception in CAVs.This work lays a foundation for integrating quantum computing into edge-based autonomous vehicle perception, highlighting both opportunities and challenges with near-term quantum hardware.
By improving their computational capacity, sensory precision, and decision-making powers, quantum technologies have the potential to completely transform autonomous car systems. These systems can handle enormous volumes of data more effectively and make choices in real time with previously unheard-of precision by utilizing quantum computing, quantum communication, and quantum sensing. While quantum sensing technologies—such as quantum radar and quantum-enhanced lidar—improve object detection and identification in difficult environments, quantum computing holds the promise of optimizing complex algorithms for path planning, traffic management, and machine learning models. the incorporation of quantum technologies into self-driving car systems, investigating how these developments can help with today's perception, navigation, and safety issues. We also talk about the possibility for quantum-enhanced autonomous systems to hasten the creation of completely autonomous transportation networks and its ramifications for the future.
Advanced Driver Assistance System (ADAS) has become an essential feature in vehicles, and it is leading to the evolution of autonomous vehicles. But the technologies to implement ADAS suffer from certain inherent limitations, such as latency rate, computational speed, accuracy of the algorithm, security, and privacy, which are also the important factors for realizing full autonomous vehicles. With respect to these hindrances, an in-depth analysis of the existing research has shown that quantum machine learning (QML) can hold a powerful and alternate solution for the development of autonomous vehicles. The perks of quantum computation (QC) over classical systems are apparent with respect to security, privacy, and an exponentially high computation rate. The current review study underlines the benefits of quantum computation and asks for more QML research to improve real-time decision-making in autonomous vehicles, ultimately improving their safety and efficiency. The promise of quantum computing to handle the massive data and computational complexity that classical methods struggle with necessitates new studies in quantum machine learning (QML) for autonomous vehicles.
Abstract: The integration of Quantum Artificial Intelligence (Quantum AI) into smart cities represents a transformative leap in traffic management and urban planning. Traditional AI-driven urban systems, while effective, struggle with real-time adaptability, large-scale data processing, and complex optimization challenges. Quantum AI, leveraging quantum superposition, entanglement, and parallel computation, enables cities to optimize traffic flow, infrastructure planning, energy management, and public safety with unparalleled speed and accuracy. This chapter explores how Quantum AI enhances predictive traffic modeling, real-time traffic light synchronization, and autonomous vehicle coordination, reducing congestion and improving mobility efficiency. Additionally, Quantum AI is reshaping urban planning by enabling smart infrastructure development, sustainable resource management, and proactive disaster response systems. Despite challenges such as high computational costs, data privacy concerns, and ethical implications, Quantum AI holds the potential to revolutionize city management, paving the way for resilient, sustainable, and highly adaptive urban environments. The future of smart cities will be defined by AI-powered quantum ecosystems, ensuring seamless urban mobility, enhanced sustainability, and intelligent decision-making for next-generation urban development. Keywords: Quantum AI, smart cities, traffic optimization, urban planning, quantum computing, real-time traffic management, predictive analytics, sustainable urban development, artificial intelligence, autonomous transportation, disaster response, smart infrastructure, quantum-enhanced mobility.