Quantum Computing for Transport Network Optimization

Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 27; no. 9; p. 953
Main Authors: Ju, Jiangwei, Liu, Zhihang, Bai, Yuelin, Wang, Yong, Gao, Qi, Ma, Yin, Zheng, Chao, Wen, Kai
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 13.09.2025
Subjects:
Algorithms
bus route optimization
Buses (vehicles)
coherent Ising machine
Combinatorial analysis
Computers
Data buses
Design optimization
Efficiency
Energy consumption
Ising model
Local transit
Network design
Network management systems
Public transportation
Quantum computers
Quantum computing
Qubits (quantum computing)
QUBO model
Route optimization
Transportation systems
China
Beijing China
bus route optimization
QUBO model
coherent Ising machine
quantum computing
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and extensive public bus network usually leads to a optimization problem characterized by high-dimensionality and non-linearity. While classical computers struggle to deal with this kind of problems, quantum computers shed new light into this field. The coherent Ising machine (CIM), a specialized optical quantum computer using a photonic dissipative architecture, has shown its remarkable computational power in combinatorial optimization problems. We construct the classical model and the quadratic unconstrained binary optimization (QUBO) model of the bus route optimization problem, and solve it using a classical computer and CIM, respectively. Our experimental results demonstrate the significant acceleration capability of CIM over classical computers in finding the optimal or near-optimal solutions, albeit subject to the hardware limitations of the 100-qubit CIM.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e27090953