Mentions of digitalization within the filings of companies in the railway industry rose 47% between the first and second quarters of 2021. Credit: Ales Krivec on Unsplash.

Cambridge Quantum (CQ) has joined forces with German Deutsche Bahn Netz AG (DB) to look into the potential of quantum computers in enhancing rescheduling of rail traffic.

This alliance has been forged under DB’s long-term transformative plan, Digitale Schiene Deutschland.

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The plan intends to digitise DB’s infrastructure and railway system utilising modern technologies.

This partnership will join DB’s operations research expertise and CQ’s combinatorial optimisation algorithm Filtering Variational Quantum Eigensolver (F-VQE).

Under this collaboration, the duo will use quantum algorithms and domain-specific modelling for a ‘faster and greener’ transportation network.

Cambridge Quantum CEO Ilyas Khan said: “We are very excited to be working with Deutsche Bahn to explore and demonstrate the utility of today’s Noisy Intermediate Scale Quantum (NISQ) processors to solve real-world problems in the transport and logistics sector.

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“Deutsche Bahn’s research and development efforts in this area are of critical importance, and we are confident that over time as quantum computers start to scale, our work with the will lead to a meaningful contribution towards a cleaner and greener future.”

Digitale Schiene Deutschland capacity and traffic management system lead Michael Küpper said: “The collaboration with CQ is a perfect example of how Deutsche Bahn is working as a partner with industry providers and combining our relative expertise towards a goal neither side can achieve alone. By working with CQ, we have fine-tuned our research and development plans and taken the first steps in defining a future quantum-advantaged train timetabling system.”

DB Netz currently provides service for 420 railway undertakings (RUs) using a course network of round 33,300km.

In February this year, Knorr-Bremse reached a cooperation agreement with Deutsche Bahn on the intelligent use of train operating and vehicle subsystem data.