# Traffic flow optimization using a quantum annealer

arXiv: https://arxiv.org/abs/1708.01625 · HTML: https://mlsanity.com/paper/1708.01625/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2017.

## Built on by (most cited first, 29 of 29)

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- [Black-box optimization for integer-variable problems using Ising machines and factorization machines](https://mlsanity.com/paper/2209.01016/) (2022)
- [Quantum Annealing for Single Image Super-Resolution](https://mlsanity.com/paper/2304.08924/) (2023)
- [Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines](https://mlsanity.com/paper/2109.10900/) (2021)
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- [Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing](https://mlsanity.com/paper/2310.12153/) (2023)
- [Training Multi-layer Neural Networks on Ising Machine](https://mlsanity.com/paper/2311.03408/) (2023)
- [Assessment of image generation by quantum annealer](https://mlsanity.com/paper/2103.08373/) (2021)
- [Quantum-Assisted Feature Selection for Vehicle Price Prediction Modeling](https://mlsanity.com/paper/2104.04049/) (2021)
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- [Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems](https://mlsanity.com/paper/2504.20660/) (2025)
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- [Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization](https://mlsanity.com/paper/2501.02114/) (2025)
- [Exact Spin Elimination for Quadratic and k-Local Ising Optimization](https://mlsanity.com/paper/2505.07163/) (2025)
- [Solving Large Break Minimization Problems in a Mirrored Double Round-robin Tournament Using Quantum Annealing](https://mlsanity.com/paper/2110.07239/) (2021)
- [Boosting the Performance of Quantum Annealers using Machine Learning](https://mlsanity.com/paper/2203.02360/) (2022)
- [Efficient correlation-based discretization of continuous variables for annealing machines](https://mlsanity.com/paper/2301.07244/) (2023)
- [Graph Coloring Using Heat Diffusion](https://mlsanity.com/paper/2404.14457/) (2024)
- [Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks](https://mlsanity.com/paper/2410.12636/) (2024)
- [A Generative AI Framework for Intelligent Utility Billing CO 2 Analytics and Sustainable Resource Optimisation](https://mlsanity.com/paper/2605.16250/) (2026)
- [Stage-dependent integer-binary encoding in factorization-machine black-box optimization](https://mlsanity.com/paper/2606.23188/) (2026)

## 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.