Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit
Abstract Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen an...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-04568-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849399819811946496 |
|---|---|
| author | Hadi Salloum Sanzhar Zhanalin Amer Al Badr Yaroslav Kholodov |
| author_facet | Hadi Salloum Sanzhar Zhanalin Amer Al Badr Yaroslav Kholodov |
| author_sort | Hadi Salloum |
| collection | DOAJ |
| description | Abstract Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe’s largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. The proposed method (MTF) enhances traffic management by decomposing the overall optimization problem into smaller, more manageable subproblems. This decomposition enables us to harness the advantages of the QPU while tackling more complex traffic scenarios that previous approaches struggled to manage. By breaking the problem into smaller parts, we mitigate the challenges associated with embedding large-scale problems into the QPU, which often presents computational difficulties. To evaluate our approach, we conducted experiments involving 100, 200, 300, 400, and 500 cars on a complex traffic map featuring multiple start and end points. We successfully embedded the problem into the D-Wave Advantage Quantum Processing Unit, utilizing the “Pegasus” topology, which resulted in a significant acceleration of the solution process. The experiment results show improved speed and effectiveness in real-world scenarios by leveraging the QPU for better traffic optimization. |
| format | Article |
| id | doaj-art-1a494a1ae76c435eb8fc3bf5ce7c8387 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-1a494a1ae76c435eb8fc3bf5ce7c83872025-08-20T03:38:15ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-04568-2Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unitHadi Salloum0Sanzhar Zhanalin1Amer Al Badr2Yaroslav Kholodov3Laboratory of Quantum Computing, Innopolis UniversityLaboratory of Quantum Computing, Innopolis UniversityLaboratory of Quantum Computing, Innopolis UniversityLaboratory of Quantum Computing, Innopolis UniversityAbstract Traffic congestion continues to pose a significant challenge in urban environments, necessitating innovative approaches to traffic management. This paper explores the application of Quantum Annealing (QA) for real-world traffic optimization, expanding on the pioneering work of Volkswagen and D-Wave. In 2017, a collaborative team demonstrated the potential of QA to optimize traffic flow by solving a complex Quadratic Unconstrained Binary Optimization (QUBO) problem involving 418 cars, which required 1,254 qubits. Later, this research culminated in a pilot project at the Web Summit conference in Lisbon, one of Europe’s largest technology events, showcasing quantum computing-based traffic optimization. Since the QPU alone could not directly handle the full problem size, the team employed a hybrid classical-quantum approach, leading to significant improvements in traffic distribution. This paper builds on that foundation by investigating potential speedups using a purely quantum approach, particularly by utilizing the QPU for smaller QUBO problems. The proposed method (MTF) enhances traffic management by decomposing the overall optimization problem into smaller, more manageable subproblems. This decomposition enables us to harness the advantages of the QPU while tackling more complex traffic scenarios that previous approaches struggled to manage. By breaking the problem into smaller parts, we mitigate the challenges associated with embedding large-scale problems into the QPU, which often presents computational difficulties. To evaluate our approach, we conducted experiments involving 100, 200, 300, 400, and 500 cars on a complex traffic map featuring multiple start and end points. We successfully embedded the problem into the D-Wave Advantage Quantum Processing Unit, utilizing the “Pegasus” topology, which resulted in a significant acceleration of the solution process. The experiment results show improved speed and effectiveness in real-world scenarios by leveraging the QPU for better traffic optimization.https://doi.org/10.1038/s41598-025-04568-2 |
| spellingShingle | Hadi Salloum Sanzhar Zhanalin Amer Al Badr Yaroslav Kholodov Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit Scientific Reports |
| title | Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit |
| title_full | Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit |
| title_fullStr | Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit |
| title_full_unstemmed | Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit |
| title_short | Mini-scale traffic flow optimization: an iterative QUBOs approach converting from hybrid solver to pure quantum processing unit |
| title_sort | mini scale traffic flow optimization an iterative qubos approach converting from hybrid solver to pure quantum processing unit |
| url | https://doi.org/10.1038/s41598-025-04568-2 |
| work_keys_str_mv | AT hadisalloum miniscaletrafficflowoptimizationaniterativequbosapproachconvertingfromhybridsolvertopurequantumprocessingunit AT sanzharzhanalin miniscaletrafficflowoptimizationaniterativequbosapproachconvertingfromhybridsolvertopurequantumprocessingunit AT ameralbadr miniscaletrafficflowoptimizationaniterativequbosapproachconvertingfromhybridsolvertopurequantumprocessingunit AT yaroslavkholodov miniscaletrafficflowoptimizationaniterativequbosapproachconvertingfromhybridsolvertopurequantumprocessingunit |