Germany’s Deutsche Bahn Netz AG (DB) partnering with Cambridge Quantum to explore how quantum computers can improve rail traffic

(TechHQ) Railway systems around the world continue to innovate new technologies to improve their services. While trains have been upgraded to provide better comfort, more connectivity, and run at faster speeds, scheduling them to be dependably on time like clockwork is always the desired outcome for all train operators.
Train scheduling may sound simple — but with passenger footfall rising, the frequency of train rides needs to increase as well. And the only way train companies can understand how to enhance rail services, is by studying the data they have with them.
However, the data may not be entirely accurate and unlikely to be in real-time. Today, train companies rely on real-time data and insights to make decisions on train schedules, for both long and short travel routes.
In Germany, Deutsche Bahn Netz AG (DB) is responsible for the rail infrastructure. DB is the service provider for currently 420 railway undertakings utilizing a route network covering nearly 33,300 km. The German railway network is the longest in all of Europe, making scheduling an important task.
To ensure prompt train scheduling, DB partnered with Cambridge Quantum (CQ) to explore how quantum computers can improve rail traffic as part of DB’s long-term transformative plan. This includes digitizing DB’s infrastructure and railway system using next-generation technologies to achieve a higher capacity and optimal utilization of the rail network.
Combining Cambridge Quantum’s latest combinatorial optimization algorithm Filtering Variational Quantum Eigensolver (F-VQE) with DB’s operations research expertise, the team re-optimized realistic train timetables after simulated delays, and are now identifying areas for continued study. This collaboration evidences how innovations in both quantum algorithms and domain-specific modeling can inform a long-term vision for a faster and greener transportation network.