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.
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.
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.
The Internet of Things (IoT) has rapidly transformed industries by enabling the interconnectivity of devices and the real-time exchange of data. However, as IoT networks continue to scale, the limitations of traditional edge and cloud computing architectures—such as latency, security concerns, and scalability challenges—become more pronounced. This article explores the emerging role of quantum computing in addressing these challenges and advancing the evolution of edge and cloud solutions for IoT systems. By leveraging the unique capabilities of quantum computing, including superposition, entanglement, and quantum parallelism, IoT systems can achieve enhanced data processing speeds, improved security, and more efficient resource management. This article discusses the potential benefits of integrating quantum computing with edge and cloud infrastructures, focusing on use cases in real-time analytics, secure communication, and advanced machine learning for IoT applications. Additionally, it examines the technical, ethical, and regulatory challenges that need to be addressed for the successful deployment of quantum-edge cloud solutions. Finally, the article highlights the importance of collaboration between academia, industry, and policymakers to drive the development of quantum-enabled IoT systems. The findings suggest that quantum computing will play a pivotal role in the next generation of IoT infrastructure, unlocking new possibilities for smarter, more responsive, and secure IoT ecosystems.
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.
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.
Quantum computing holds transformative potential for Intelligent Transportation Systems (ITS), offering solutions to complex and computationally intensive challenges in modern transportation networks. By leveraging quantum mechanics principles like superposition and entanglement, quantum algorithms can optimize traffic management, route planning, and logistics with unprecedented speed and accuracy. This can significantly enhance real-time traffic flow predictions, dynamic route optimization, and efficient resource allocation, particularly for electric vehicle networks, promoting sustainable and energy-efficient transportation solutions. However, integrating quantum computing into ITS faces challenges. Current quantum hardware is limited by issues like qubit instability, short coherence times, and high error rates, impeding large-scale applications. Additionally, developing hybrid systems that combine classical and quantum computing capabilities, and ensuring robust data privacy and security, are significant hurdles.
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.