Quantum Computing for Intelligent Transportation Systems: VQE-Based Traffic Routing and EV Charging Scheduling

Uman Khalid, Usama Inam Paracha, Syed Muhammad Abuzar Rizvi and Hyundong Shin ()
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Uman Khalid: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Usama Inam Paracha: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Syed Muhammad Abuzar Rizvi: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea
Hyundong Shin: Department of Electronics and Information Convergence Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

Mathematics, 2025, vol. 13, issue 17, 1-12

Abstract: Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions.

Keywords: intelligent transportation systems; quantum computing; variational quantum optimization algorithms (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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