Combinatorial Speedup
Reduce real-world train rerouting computation times from hours to minutes/seconds.
Railway Optimization
Industry Sector: Transportation & Mobility
Lead Beneficiary: DB Systel
The Industry Challenge
Modern railway networks operate near maximum capacity. Managing train dispatching, real-time delay management, and rolling stock allocation involves high-dimensional combinatorial optimization problems. When unexpected disruptions occur, classical solvers often take hours to calculate alternative routes, leading to propagated delays, inflated operational costs, and excessive energy consumption.
The Approach
DB Systel leverages the Q-VERSE framework to deploy hybrid quantum-classical optimization algorithms built on Eclipse Qrisp.
Hybrid Solvers: Combining EuroHPC classical compute nodes with gate-based and annealing QPUs for sub-microsecond schedule recalculations.
Data Sovereignty: Utilizing GAIA-X data space connectors to securely stream live transport and signaling telemetry without exposing sensitive infrastructure data.
Key Objectives & Expected Impact
Energy Efficiency
Minimize idle train waiting times and optimize traction energy consumption.
Scalable SDK Integration
Demonstrate that railway engineers can write quantum-boosted solvers using standard Python abstractions without low-level QPU coding.