QUEST (QUantum Enhancement for Smart Transport)
In recent years, the Automotive and Transportation sectors have been moving rapidly toward vehicle-to-infrastructure connectivity, paving the way for Smart Roads and Cooperative Intelligent Transport Systems (C-ITS). In this context, our project aims to develop intelligent software systems for real-time traffic management and next-generation mobility services. The core innovation lies in designing hybrid classical-quantum algorithms that enhance performance and efficiency in complex optimization tasks relevant to traffic systems. These algorithms will be integrated into scalable cloud-based platforms via well-documented APIs and accompanied by consulting services to support businesses in adopting these technologies.
Smart Roads are transforming the transportation landscape by enabling connected infrastructure and vehicles (CAVs) to interact in real time. Supported by low-latency networks (e.g., 5G) and advanced on-board systems (ADAS), these technologies promise safer, more efficient roads for all users, including Vulnerable Road Users (VRUs) like pedestrians, cyclists, and motorcyclists. However, managing traffic flows, path planning, collision avoidance, and eco-routing in real time across densely populated networks introduces significant computational challenges, including:
- Processing large-scale multivariable optimization problems (e.g., vehicle positions, speeds, acceleration profiles);
- Predicting traffic states and driver behavior in dynamically changing conditions;
- Ensuring ultra-low latency (under 100ms per vehicle) to meet C-ITS safety requirements.
Traditional computing struggles to keep up with these demands, especially as the system complexity grows. This creates a unique opportunity to leverage Quantum Computing (QC) and explore its potential. The main goal of this project is to design, test, and deploy quantum-enhanced software solutions that overcome current computational limits in Smart Mobility. Specifically, we aim to:
- Enhance and extend classical algorithms using quantum subroutines embedded in hybrid workflows;
- Apply these solutions to C-ITS tasks such as eco-routing, eco-driving, collision avoidance, and intersection traffic flow optimization;
- Create a real-time decision support system based on IoT data from fixed (e.g., traffic lights) and mobile nodes (e.g., vehicles, VRUs);
- Enable predictive and adaptive services, including incident and hazard alerts, path planning for safe maneuvers (e.g., overtaking, merging, crossing), adaptive speed limit recommendations, traffic simulations and forecasting using Digital Twin paradigms.
The hybrid algorithms will be validated through simulations and real-world traffic data, measuring key performance metrics such as computational complexity and optimization efficiency.
To enable cutting-edge performance, our development will be supported by access to state-of-the-art quantum processors, including IBM Quantum devices (via the IBM Quantum Startup Program) and the recently inaugurated 24-qubit superconducting quantum computer at the University of Naples “Federico II”, expected to reach 40 qubits in the short term. This project is aligned with both national and European strategic priorities in digital transformation and sustainable mobility. By bridging classical and quantum computing for next-gen transport systems, this project positions itself at the frontier of technological, economic, and environmental innovation.