Traffic flow optimization using a quantum annealer

TL;DR

This paper demonstrates how quantum annealing can be applied to real-world traffic flow optimization, using hybrid quantum-classical methods to address current hardware limitations and improve traffic management.

Contribution

It presents a novel approach to map traffic flow problems onto quantum annealers and showcases a hybrid method for practical, time-critical traffic optimization tasks.

Findings

Quantum annealing can be applied to real-world traffic problems.

Hybrid quantum-classical methods address hardware limitations.

Time-critical traffic optimization benefits from quantum approaches.

Abstract

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.