Research track · Energy
Energy & smart-grid optimization
Explore unit commitment and grid-scheduling formulations.
Why this horizon. Unit commitment and grid scheduling are large constrained problems where classical solvers are mature; quantum formulations are early research.
- The problem
- Grid scheduling involves large, constrained optimization under uncertainty.
- Why quantum or hybrid methods may be relevant
- Studied as a candidate domain for hybrid optimization methods.
- Classical baseline
- Industry-standard classical optimization tools.
- What QAISS could provide
- Encodings and benchmark scenarios for research use.
- What evidence would count as success
- Measured comparison on public test systems.
- Limitations & open questions
- Research setting only; not suitable for operational grid decisions.
- Current status
- Planned No experiments have been published for this track yet.
Workflow on QAISS
From question to checkable result.
- STEP 1
Take a public test system and write unit commitment as a mixed-integer problem.
- STEP 2
Reduce a small sub-problem to a QUBO.
- STEP 3
Compare a QAOA run with the classical solver on the same sub-problem.
- STEP 4
Document where the formulation breaks down as the system grows.
Interactive · runs in your browser
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.
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.