mlsanityData to 7 Oct 2026

Traffic flow optimization using a quantum annealer

arXiv 1708.01625 ↗First posted 4 Aug 2017quant-ph cs.DScited by 29

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Abstract (arXiv)

Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology's usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of the real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum applications. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Aug 2017.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 4; 2024: 5; 2025: 4; 2026: 2.

29 papers.

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  1. 2024Comprehensive Overview of Reward Engineering and Shaping in Advancing Reinforcement Learning Applicationscited by 34
  2. 2021Quantum Permutation Synchronizationcited by 32
  3. 2022Adiabatic Quantum Computing for Multi Object Trackingcited by 22
  4. 2021Adiabatic Quantum Graph Matching with Permutation Matrix Constraintscited by 20
  5. 2022Application of QUBO solver using black-box optimization to structural design for resonance avoidancecited by 17
  6. 2022Hybrid quantum ResNet for car classification and its hyperparameter optimizationcited by 17
  7. 2022The role of entanglement for enhancing the efficiency of quantum kernels towards classificationcited by 10
  8. 2022Black-box optimization for integer-variable problems using Ising machines and factorization machinescited by 8
  9. 2023Quantum Annealing for Single Image Super-Resolutioncited by 5
  10. 2021Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machinescited by 4
  11. 2025SWIFT-FMQA: Enhancing Factorization Machine with Quadratic-Optimization Annealing via Sliding Windowcited by 4
  12. 2022QuAnt: Quantum Annealing with Learnt Couplingscited by 3
17 more
  1. 2023Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computingcited by 3
  2. 2023Training Multi-layer Neural Networks on Ising Machinecited by 3
  3. 2021Assessment of image generation by quantum annealercited by 2
  4. 2021Quantum-Assisted Feature Selection for Vehicle Price Prediction Modelingcited by 2
  5. 2024Efficient Combinatorial Optimization via Heat Diffusioncited by 2
  6. 2024Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machinescited by 2
  7. 2021Quantum Computing for Artificial Intelligence Based Mobile Network Optimizationcited by 1
  8. 2025Relaxation-assisted reverse annealing on nonnegative/binary matrix factorizationcited by 1
  9. 2025Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systemscited by 1
  10. 2025Exact Spin Elimination for Quadratic and k-Local Ising Optimizationcited by 1
  11. 2021Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum Annealingcited by 0
  12. 2022Boosting the Performance of Quantum Annealers using Machine Learningcited by 0
  13. 2023Efficient correlation-based discretization of continuous variables for annealing machinescited by 0
  14. 2024Graph Coloring Using Heat Diffusioncited by 0
  15. 2024Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networkscited by 0
  16. 2026A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisationcited by 0
  17. 2026Stage-dependent integer-binary encoding in factorization-machine black-box optimizationcited by 0