Quantum

Quantum approximate stochastic combinatorial optimization

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 19/10/2026, 04:13:25 UTC.

Briefings

A new quantum algorithm called QASCO uses variational quantum walks to solve two-stage stochastic combinatorial optimisation problems. The method coherently incorporates scenario-dependent recourse costs into its dynamics, avoiding explicit evaluation of expected minimum recourse costs. For MaxCut with stochastic edge weights, QASCO concentrates amplitude more effectively on stochastic-optimal first-stage decisions than a previously proposed two-stage QAOA construction. The algorithm achieves polynomial per-layer gate complexity and can be calibrated with a depth-independent number of parameters.

Key points

• QASCO uses variational quantum walks to coherently incorporate scenario-dependent recourse costs into its dynamics.
• QASCO concentrates amplitude more effectively on stochastic-optimal first-stage decisions than a previously proposed two-stage QAOA construction for MaxCut with stochastic edge weights.

Context

Quantum approximate stochastic combinatorial optimisation addresses two-stage problems where initial decisions are made before uncertainty is resolved, allowing adaptation at a cost. This work introduces QASCO as a variational quantum-walk ansatz that coherently incorporates scenario-dependent recourse into its dynamics to search over first-stage decisions without explicitly evaluating expected minimum recourse costs.

Limitations

Results are based on exact state-vector simulation and limited to instances considered in the study. The algorithm was tested on MaxCut with stochastic edge weights and compared against classical heuristics using sample-average approximations of expected minimum recourse costs. No additional background on the broader applicability or performance on unseen instances beyond the cited instances is supplied.

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