Research track · Logistics & operations
Scheduling & logistics optimization
Benchmark hybrid heuristics on routing and scheduling instances.
Why this horizon. Hardware QAOA experiments exist at small scale; classical heuristics remain far ahead on realistic routing and scheduling instances.
- The problem
- Routing and scheduling at scale rely on heuristics with hard-to-measure gaps.
- Why quantum or hybrid methods may be relevant
- Hybrid quantum–classical heuristics are an open research area.
- Classical baseline
- State-of-the-art classical heuristics and MIP solvers.
- What QAISS could provide
- Standard instance sets and reproducible comparison runs.
- What evidence would count as success
- Documented quality/runtime trade-offs on public instances.
- Limitations & open questions
- Most published results remain at small scale; advantage is not assumed.
- Current status
- Planned No experiments have been published for this track yet.
Workflow on QAISS
From question to checkable result.
- STEP 1
Encode routing or scheduling as a graph or QUBO problem.
- STEP 2
Establish the classical baseline (heuristics, MIP solvers).
- STEP 3
Run QAOA at depth p on a simulator; study how quality changes with p and noise.
- STEP 4
Publish instance, parameters and passports for comparison.
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Why optimisation problems get hard
- Possible combinations
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- Check them all at 10⁹ per second
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- Qubits for a direct QAOA encoding
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Exhaustive search explodes quickly, but nobody solves real instances that way: classical heuristics and MIP solvers find good answers on problems with thousands of variables. QAOA needs one qubit per decision and, so far, has not beaten those solvers. That is what a fair comparison has to show. In the Lab, the QAOA preset compares a depth-1 run with the exhaustive classical answer on a 3-node graph.
Sources
Read the literature.
- A quantum approximate optimization algorithmE. Farhi, J. Goldstone, S. Gutmann · arXiv:1411.4028 · 2014
- Quantum approximate optimization of non-planar graph problems on a planar superconducting processorM. P. Harrigan, K. J. Sung, M. Neeley et al. · Nature Physics 17, 332–336 · 2021
Cited for context. QAISS is not affiliated with the authors or publishers.
No quantum advantage, production savings, medical benefit, financial return, or security guarantee is claimed for this track.