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- Title
High-speed train timetable optimization based on space–time network model and quantum simulator.
- Authors
Xu, Hui-Zhang; Chen, Jun-Hua; Zhang, Xing-Chen; Lu, Te-Er; Gao, Tian-Ze; Wen, Kai; Ma, Yin
- Abstract
Timetable scheduling is a combinatorial optimization problem that presents formidable challenges for classical computers. This paper introduces a pioneering methodology for addressing the high-speed train timetabling problem through quantum computing. Initially, a comprehensive binary integer programming model, grounded in the space–time network, is proposed (M1). To manage the intricacy of model M1, a knapsack problem reformulation is employed to establish a simplified binary integer programming model (M2). Both M1 and M2 are subsequently converted into quadratic unconstrained binary optimization (QUBO) models to harness the potential of quantum computing. Several techniques, including the Gurobi solver, simulated annealing, and the coherent Ising machine (CIM) quantum simulator, are deployed to solve the model across four distinct scenarios of varying complexity. The findings indicate that CIM quantum simulator outperforms the simulated annealing method in terms of solution quality for medium-scale problems.
- Subjects
HIGH speed trains; QUANTUM computing; TIME perspective; KNAPSACK problems; SPACETIME; INTEGER programming; QUANTUM computers; HOPFIELD networks
- Publication
Quantum Information Processing, 2023, Vol 22, Issue 11, p1
- ISSN
1570-0755
- Publication type
Article
- DOI
10.1007/s11128-023-04170-3