Research Project
QLMD: Quantum Computing for an efficient urban logistical ecosystem
Applying quantum computing to urban logistics to optimize routes, reduce costs and emissions, improve urban sustainability, and tackle complex problems with scalable solutions.
Where: Italy
Where: Italy
Challenge
In logistics ecosystems, issues arise related to the variety of routes and operational constraints. In this context, advancements in quantum computing are expected to enable much more efficient processing of data on these ecosystems, optimizing results based on customer needs and simultaneously evaluating potential solutions.
Approach
QLMD offers an innovative solution to urban logistics challenges, including waste collection, last-mile delivery, the Traveling Salesman Problem (TSP), and the Vehicle Routing Problem (VRP). The project enables the minimization of operational costs, time, distance, traffic congestion, and fuel consumption despite logistical constraints. It also enhances urban sustainability by reducing traffic and emissions and leveraging predictive analytics to efficiently manage delivery schedules, urban restrictions, and traffic regulations.
Digital Ecosystem
Solution
Digital Ecosystem
QLMD introduces optimization algorithms capable of reducing waste and emissions, thereby contributing to sustainability. The project focuses on developing practical and implementable use cases to guide the creation of a solid quantum strategy. By refining data modeling techniques, it deepens the understanding of complex problems such as VRP and TSP, adopting approaches inspired by quantum technology. Additionally, it creates a series of datasets to implement the algorithms, evaluate the proposed solutions, and develop a scalable quantum solution to address NP-hard problems.
Engineering is responsible for the development of quantum computing algorithms for application to TSP problems.
The research project is funded by the National Recovery and Resilience Plan (PNRR), Mission 4 Component 2.
SCIENTIFIC PAPERS
The QLMD research project led to the publication of two scientific papers dedicated to the application of quantum algorithms to the TSP. The papers describe the technical approach, developed within a simulated High‑Performance Computing (HPC) environment and based on the Quantum Approximate Optimization Algorithm (QAOA) and its hybrid extension (Cl‑QAOA). The latter, leveraging AI techniques, makes it possible to decompose the TSP into more manageable sub‑problems, improving scalability and performance. Together, the two studies provide a comprehensive overview of the potential of quantum computing for the logistics of the future.
Expected Results
Increased efficiency of logistics ecosystems
Reduced operating costs
Impacts
Technologies
AI & Data
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