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The IEEE Quantum Week 2026 panel on logistics revealed that while quantum annealers and gate-based QAOA methods can handle simplified versions of complex planning problems like railway scheduling, classical solvers remain superior for real-world applications once constraints are fully modeled. The consensus from nine papers is that the "quantum step" often shrinks to negligible margins as problem fidelity increases, suggesting current hardware lacks a practical advantage over optimized classical algorithms for large-scale logistics optimization.
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Trains, floods and wedding guests tested quantum computing for logistics at QCE26

Quantum Zeitgeist19 min read

Deutsche Bahn trains, flood defences, database queries and a wedding chart tested quantum computing for logistics at IEEE Quantum Week 2026.

This guide follows nine papers and one panel on quantum computing for logistics and planning from IEEE Quantum Week 2026 in Toronto. Teams cut real jobs down until a quantum step could cope, and then said plainly where classical solvers still won. In 1950 the Canadian National Exhibition in Toronto put on show Bertie the Brain, a machine that played tic-tac-toe, which Vikram Mulligan at QCE26 credited to Josef Kates. Seventy-six years later, in the same city, Mulligan had a D-Wave quantum annealer play the same game. Moving first, it drew 28 of 30 games against a perfect classical player. Tic-tac-toe is a fair warm-up for quantum computing for logistics. Its board is small and its rules are few. The planning jobs that railways, water boards and databases face are the same kind of puzzle on a far larger board. Each is a long list of yes-or-no choices. A railway picks which train set runs which cycle of trips, and a water board picks which rivers get protection. Every choice added doubles the number of possible plans, so fifty choices give about a million billion, far too many to check one by one. That is why quantum computing for logistics…

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