Insider Brief
- IQM and Deutsche Bahn demonstrated a hybrid quantum-classical approach that used real railway scheduling data to solve a large-scale optimization problem on current quantum hardware, providing a framework that could be applied across industries.
- The project used a real Deutsche Bahn dataset covering 190 trips across five German cities and about 98,500 possible scheduling cycles, with QAOA solving smaller optimization subproblems within a larger classical workflow.
- The researchers found the approach produced feasible solutions on today’s hardware, improved as larger quantum subproblems could be processed, and completed the full optimization pipeline on an IQM quantum computer.
PRESS RELEASE — IQM Quantum Computers (Nasdaq: IQMX), a global leader in full-stack superconducting quantum computers, today published the results of a research collaboration with Deutsche Bahn, Europe’s largest rail operator, exploring how quantum computing can improve railway scheduling.
Using a real operational dataset from Deutsche Bahn, a schedule of 190 trips across five German cities translating into roughly 98,500 possible cycles, the two organizations developed and tested a hybrid quantum-classical algorithm designed for enterprise-scale optimization problems.
As detailed in a published whitepaper here, the IQM team applied the Quantum Approximate Optimization Algorithm (QAOA) in manageable stages, with the quantum component solving smaller subproblems within a classical framework that manages the problem at full scale. The architecture is built to generalize: the same approach can be applied to comparable optimization challenges in logistics, energy, manufacturing and other sectors.
Three results stand out:
It works on today’s hardware. The approach delivered feasible, good-quality solutions without requiring hardware that does not yet exist. Enterprises can adopt this model now rather than waiting for fault-tolerant systems.
It improves automatically as hardware improves. Testing showed a statistically significant relationship between the size of the subproblem the quantum component could handle and the quality of the solution. As quantum processors scale, the same framework is expected to deliver better outcomes without redesign.
It ran end to end on IQM hardware. The full pipeline was executed on an IQM quantum computer, from problem formulation through to a usable result, establishing a baseline that enterprises and partners can build on.
“This collaboration shows that quantum computing is already capable of tackling the kind of large-scale, real-world optimization problems enterprises face every day,” said Dr. Inés de Vega, Chief Scientist of IQM Quantum Computers. “Working with Deutsche Bahn on a problem of this complexity gives us a clear blueprint for how quantum computing delivers value now, while scaling naturally as hardware improves.”
“Quantum computing is not going away. By tackling a real-world problem in a hybrid HPC and quantum computing environment, we have taken another step toward quantum advantage,” said Manfred Rieck, Head of Quantum Technology at Deutsche Bahn.
This collaboration focused on planning under known, stable conditions. Many enterprises face a faster-moving version of the same challenge: responding to disruptions in real time, on timescales of minutes rather than hours. The researchers note that the same hybrid architecture could, in principle, be adapted to this more time-sensitive class of problem as quantum hardware improves, extending the approach further into operational decision-making.
The collaboration reflects IQM’s focus on enterprise adoption of quantum computing through systems that customers own, operate and build on.
