Abstract Quantum computing has the potential to transform the classical computing paradigm with improved efficiency for solving NP-hard combinatorial optimization problems modeled as a quadratic unconstrained binary optimization (QUBO) model, including those in transportation and supply chains. Although the quantum annealing (QA) algorithm is theoretically attractive, there is a lack of computational experience showing its superior performance over the traditional algorithms. The purpose of this project is to explore quantum computing and quantum-inspired algorithms on a class of transportation network design problems. We will develop and implement custom-designed algorithms to solve large-scale QUBO models for transportation network design, and evaluate their performance compared to that of QA. Our model and algorithms are expected to provide optimal large-scale network design solutions efficiently. This project aligns with the DOT’s strategic goal of economic strength and global competitiveness, and supports MATC-TSE’s theme on transportation systems of the future.
Description Quantum computing has the potential to transform the classical computing paradigm with improved efficiency for solving NP-hard combinatorial optimization problems modeled as a quadratic unconstrained binary optimization (QUBO) model, including those in transportation and supply chains. Although the quantum annealing (QA) algorithm is theoretically attractive, there is a lack of computational experience showing its superior performance over the traditional algorithms. The purpose of this project is to explore quantum computing and quantum-inspired algorithms on a class of transportation network design problems. We will develop and implement custom-designed algorithms to solve large-scale QUBO models for transportation network design, and evaluate their performance compared to that of QA. Our model and algorithms are expected to provide optimal large-scale network design solutions efficiently. This project aligns with the DOT’s strategic goal of economic strength and global competitiveness, and supports MATC-TSE’s theme on transportation systems of the future.
Objective This proposed project aligns with the following US DOT Strategic Goal. Economic Strength and Global Competitiveness: Quantum computing represents one of the most exciting new technologies in computing and data science. The QUBO models and algorithms developed in this project have the potential to improve the solution quality and efficiency for designing large-scale transportation networks in manufacturing, agriculture, and healthcare, which has significant economic impact and enhances the global the global competitiveness of the U.S. businesses.
Impacts/Benefits Our models and algorithms are expected to improve the state-of-the-art of transportation network optimization. They can also be applied for solving other combinatorial optimization problems beyond the transportation network design problems studied in this project.