Research track · Finance
Portfolio optimization & risk-model research
Research QAOA-style formulations with classical solvers alongside.
Why this horizon. QAOA-style formulations are actively studied, but no practical advantage over classical optimisers has been shown on real portfolio problems.
- The problem
- Constrained portfolio problems become combinatorially hard at scale.
- Why quantum or hybrid methods may be relevant
- Some optimization problems map naturally to quantum formulations worth studying.
- Classical baseline
- Commercial and open-source classical optimizers on the same instances.
- What QAISS could provide
- Problem encodings, hybrid optimization templates, and comparison reports.
- What evidence would count as success
- Solution quality and time-to-solution reported against the classical baseline.
- Limitations & open questions
- Research only. No financial return, trading signal, or advice is offered.
- Current status
- Planned No experiments have been published for this track yet.
Workflow on QAISS
From question to checkable result.
- STEP 1
Write the selection problem as a QUBO (risk, return, budget penalty).
- STEP 2
Solve it classically first: exhaustive for tiny instances, MIP or annealing heuristics at scale.
- STEP 3
Run a shallow QAOA on a simulator, then optionally on hardware.
- STEP 4
Report solution quality and time-to-solution against the classical result.
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.
- A quantum approximate optimization algorithmE. Farhi, J. Goldstone, S. Gutmann · arXiv:1411.4028 · 2014
- A survey of quantum computing for financeD. Herman, C. Googin, X. Liu et al. · arXiv:2201.02773 · 2022
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.