As part of a pilot project, IQM Quantum Computers, in collaboration with Deutsche Bahn, successfully tested a hybrid quantum-classical algorithm designed for railway scheduling. The testing was conducted using real operational data from the German railway operator, lending the results particular practical significance.
How the hybrid approach works
The developed algorithm uses a combination of classical computing power and quantum processors. The quantum computer, in this case IQM hardware, solved small but highly complex fragments of the rolling stock allocation problem. The bulk of the calculations—managing overall planning—was handled by a traditional system. This hybrid method made it possible to generate feasible schedule options that meet all operational constraints.
Why this matters for the industry
Optimizing railway schedules is a classic NP-hard combinatorial optimization problem, which traditional algorithms struggle with as network scale increases. Quantum computing, even at its current stage of development, can offer significant acceleration for subtasks such as assigning trains to tracks and time slots. The success of the test using real Deutsche Bahn data is an important step toward integrating quantum technologies into transportation logistics.
My expert assessment: This experiment confirms that hybrid quantum-classical systems are the most realistic path to commercializing quantum computing in the coming years. A complete replacement of classical systems is not yet possible, but targeted application of quantum algorithms to solve particularly complex optimization problems already delivers measurable business benefits today. Deutsche Bahn has chosen the right strategy by not attempting to "quantize" the entire process, but instead integrating the new technology into existing infrastructure.