IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
A Quantum-Enhanced Hybrid Framework for Improving Traffic Resilience in Large-Scale Events
Enmao Zhang, Jianxun Ding, Xingbin Zhan, Zhiguo Huang, Fei Yao, Gangqiang Xu, Xinghua Hou, Chaolun Wang, Xuan Cui
The management of large-scale events poses significant resilience challenges to urban traffic systems, where classical computational methods often prove inadequate for the resulting complex, dynamic optimization problems. This paper introduces a quantum-enhanced hybrid high-performance computing framework designed to improve traffic resilience during such events. The framework leverages a Quantum Annealing (QA) algorithm to solve a Quadratic Unconstrained Binary Optimization (QUBO) model of key traffic optimization tasks, integrated with classical computing for data processing and workflow control. Using the traffic management for a major concert at Hefei Luogang Park as a case study, we focus on modeling and applying this framework to two core resilience problems: Dynamic Evacuation Route Optimization (DERO) and Emergency Resource Allocation (ERA). Our simulation results provide empirical validation of the framework's effectiveness, demonstrating that the proposed quantumenhanced approach reduces total evacuation time by 20.0 % and average emergency response time by 32.2 % compared to welldefined greedy heuristics. These findings highlight the potential of quantum computing as an emerging high-performance computing paradigm for addressing the intricate challenges of traffic resilience.
The management of large-scale events poses significant resilience challenges to urban traffic systems, where classical computational methods often prove inadequate for the resulting complex, dynamic optimization problems. This paper introduces a quantum-enhanced hybrid high-performance computing framework designed to improve traffic resilience during such events. The framework leverages a Quantum Annealing (QA) algorithm to solve a Quadratic Unconstrained Binary Optimization (QUBO) model of key traffic optimization tasks, integrated with classical computing for data processing and workflow control. Using the traffic management for a major concert at Hefei Luogang Park as a case study, we focus on modeling and applying this framework to two core resilience problems: Dynamic Evacuation Route Optimization (DERO) and Emergency Resource Allocation (ERA). Our simulation results provide empirical validation of the framework's effectiveness, demonstrating that the proposed quantumenhanced approach reduces total evacuation time by 20.0 % and average emergency response time by 32.2 % compared to welldefined greedy heuristics. These findings highlight the potential of quantum computing as an emerging high-performance computing paradigm for addressing the intricate challenges of traffic resilience.
Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe's largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. The proposed method (MTF) enhances traffic management by decomposing the overall optimization problem into smaller, more manageable subproblems. This decomposition enables us to harness the advantages of the QPU while tackling more complex traffic scenarios that previous approaches struggled to manage. By breaking the problem into smaller parts, we mitigate the challenges associated with embedding large-scale problems into the QPU, which often presents computational difficulties. To evaluate our approach, we conducted experiments involving 100, 200, 300, 400, and 500 cars on a complex traffic map featuring multiple start and end points. We successfully embedded the problem into the D-Wave Advantage Quantum Processing Unit, utilizing the "Pegasus" topology, which resulted in a significant acceleration of the solution process. The experiment results show improved speed and effectiveness in real-world scenarios by leveraging the QPU for better traffic optimization.
Traffic congestion remains a significant challenge in urban environments, necessitating innovative solutions for efficient traffic management. This paper explores the use of quantum annealing for traffic optimization, building on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of quantum annealing to optimize traffic flow by addressing a complex 418-car Quadratic Unconstrained Binary Optimization problem. Using a hybrid classical and quantum computing approach, they achieved notable improvements in traffic distribution. In this paper, we introduce a novel approach: Quantum Congestion-Focused Traffic Optimization (Q-CFTO). Our approach is designed to enhance traffic management optimization by breaking down the problem of optimizing traffic flow into smaller and more manageable subproblems rather than dealing with a single large optimization challenge. This decomposition facilitates the handling of the problem's complexity as embedding a large problem into a Quantum Processing Unit can be difficult. We successfully embedded the problem directly into the D-Wave Advantage Quantum Processing Unit with the "Pegasus" topology, significantly accelerating the solution process. The results demonstrate that our approach is both faster and more effective in real-world scenarios.
Traffic congestion is one of the major issues in urban areas, particularly when traffic loads exceed the road’s capacity, resulting in higher petrol consumption and carbon emissions as well as delays and stress for road users. In Asia, the traffic situation can be further deteriorated by road sharing of scooters. How to control the traffic flow to mitigate the congestion has been one of the central issues in transportation research. In this study, we employ a quantum annealing approach to optimize the traffic signals control at a real-life intersection with mixed traffic flows of vehicles and scooters. Considering traffic flow is a continuous and emerging phenomenon, we used quadratic unconstrained binary optimization (QUBO) formalism for traffic optimization, which has a natural equivalence to the Ising model and can be solved efficiently on the quantum annealers, quantum computers or digital annealers. In this article, we first applied the QUBO traffic optimization to artificially generated traffic for a simple intersection, and then we used real-time traffic data to simulate a real "Dongda-Keyuan" intersection with dedicated cars and scooter lanes, as well as mixed scooter and car lanes. We introduced two types of traffic light control systems for traffic optimization: C-QUBO and QUBO. Our rigorous QUBO optimizations show that C-QUBO and QUBO outperform the commonly used fixed cycle method, with QUBO outperforming C-QUBO in some instances. It has been found that QUBO optimization significantly relieves traffic congestion for the unbalanced traffic volume. Furthermore, we found that dynamic changes in traffic light signal duration greatly reduce traffic congestion.
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.
Cloud computing has transformed modern digital infrastructure by enabling on-demand access to scalable computational resources. However, the increasing complexity of dynamic workloads and the need for efficient resource allocation present persistent challenges in maintaining performance, cost efficiency, and energy sustainability. This paper introduces a Quantum-Driven Optimization (QDO) framework, a hybrid quantum-classical approach designed to enhance cloud resource allocation by integrating quantum computing techniques with classical optimization methods. The proposed framework leverages Quantum Annealing (QA) and the Variational Quantum Eigensolver (VQE) to optimize resource distribution, minimizing energy consumption and operational costs while maximizing throughput and utilization efficiency. Experimental evaluations demonstrate that the QDO framework achieves a 27% improvement in resource utilization, a 34% reduction in operational costs, and a 21% enhancement in task completion time compared to traditional heuristic-based approaches. Additionally, the hybrid model reduces Service Level Agreement (SLA) violations by 18%, ensuring robust Quality of Service (QoS) for cloud users. The framework employs classical algorithms for preprocessing and decision-making while delegating complex optimization tasks to quantum solvers, ensuring scalability across diverse cloud environments. This study highlights the transformative potential of hybrid quantum-classical computing in addressing cloud resource allocation challenges. The results indicate significant improvements in energy efficiency, cost-effectiveness, and system responsiveness, making the QDO framework a viable solution for next-generation cloud infrastructures. Future research directions include extending the framework to multi-cloud architectures and investigating advanced quantum algorithms for further optimization gains.
The Traffic Assignment Problem (TAP) is a complex transportation optimisation problem typically solved using meta-heuristics on classical computers. Quantum computers, despite being a nascent technology, have the potential to significantly speed up computation by exploiting quantum parallelism. A quantum annealer (QA) is a quantum computer tailored to solve combinatorial optimisation problems formulated as a Quadratic Unconstrained Binary Optimisation (QUBO). Formulating complex optimisation problems as QUBO is an open challenge. This paper derives a new QUBO formulation for TAP by employing a streamlined methodology of general applicability. It also attempts a direct comparison at solving TAP encompassing a QA (D-WAVE), a hybrid quantum-classical algorithm, and classical methods including Simulated Annealing and Genetic Algorithms. This comparison is difficult and seldom done due to the inherent differences between quantum and classic hardware. As expected from the current quantum technology, our results show that a pure QA suffers from significant noise in qubits and requires significant additional computational time, although we show that the time required solely by the QPU does not increase with problem size. We also show that the hybrid QA mitigates these noise issues and is on a par with traditional methods.