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

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 explore...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Mathematics (Basel) Ročník 13; číslo 17; s. 2761
Hlavní autori: Khalid, Uman, Paracha, Usama Inam, Rizvi, Syed Muhammad Abuzar, Shin, Hyundong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.09.2025
Predmet:
Algorithms
Design
Efficiency
Electric vehicle charging
Electric vehicles
Forecasts and trends
Integrated circuits
Intelligent transportation systems
Interoperability
Ising model
Linear programming
Mathematical optimization
Methods
Microprocessors
Optimization
Optimization algorithms
Optimization techniques
Quantum computing
Route optimization
Route selection
Scheduling
Semiconductor chips
Traffic congestion
Transportation
Transportation industry
variational quantum optimization algorithms
ISSN:2227-7390, 2227-7390
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2227-7390
2227-7390
DOI:10.3390/math13172761