A new study by scientists at the University of Melbourne and auto maker Ford uses quantum physics to tackle the chaos of daily gridlock. The joint project published this month via arXiv explores how quantum algorithms could one day help cities streamline traffic in real time, potentially reducing commute times and emissions. At the heart […]
A new study by scientists at the University of Melbourne and auto maker Ford uses quantum physics to tackle the chaos of daily gridlock.
The joint project published this month via arXiv explores how quantum algorithms could one day help cities streamline traffic in real time, potentially reducing commute times and emissions.
At the heart of the effort is the Quantum Approximate Optimisation Algorithm (QAOA), a hybrid quantum-classical method designed to solve optimisation problems—like deciding how to route thousands of cars through city streets with minimal delays.
Traffic management is notoriously difficult, especially when trying to minimise congestion across complex networks.
To simplify the problem, researchers mapped traffic flows to an “Ising model”—a mathematical framework often used in quantum studies. They then applied QAOA, which works by tweaking a set of parameters until it homes in on the best solution.
However, today’s quantum machines—known as Noisy Intermediate-Scale Quantum (NISQ) devices—are still limited by noise and circuit complexity.
To overcome these barriers, the team introduced two workarounds. First, they used specially tailored starting points for the algorithm, helping it find solutions faster without getting lost in dead ends.
Second, they developed a noise-resilient version of QAOA that avoids tricky long-distance quantum interactions, which are prone to errors on current hardware.
Testing their innovations on IBM’s quantum computers, the researchers found their method not only produced better traffic solutions than standard QAOA, but also ran faster and more reliably on real-world quantum machines.
While the technology isn’t ready for live deployment just yet, this research marks a compelling proof of concept for how quantum computing could soon tackle tangible, real-life problems.
