Quantum

Scaling Laws for Parameter Setting in Digitized Counterdiabatic QAOA

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Briefings

A new study presents empirical scaling laws for parameter setting in digitized counterdiabatic QAOA for weighted MaxCut problems. The research shows that optimized angles cluster around common amplitudes, allowing a small coefficient table to guide parameter reuse across different graph instances. In ideal-circuit simulations, this method outperforms fixed-angle QAOA at depths one through four, achieving up to 19.36% higher mean optimal-solution probability compared to 13.11%. Hardware experiments on IBM Quantum processors also demonstrated advantages in specific graph configurations.

Key points

• The study developed empirical scaling laws for parameter setting in digitized counterdiabatic QAOA for weighted MaxCut problems.
• In ideal-circuit simulations, the method achieved a mean optimal-solution probability of 19.36% at four layers, significantly higher than the 13.11% achieved by fixed-angle QAOA.

Context

This research addresses the limitation of optimizing parameters separately for each problem instance, which restricts the reuse of variational quantum algorithms. The findings provide a practical framework for parameter reuse across graph instances, enabling direct parameter assignment without target-instance variational optimization.

Limitations

Results are based on ideal-circuit simulations and limited hardware execution on a single processor. The study covers 1050 test graphs across ten families and 600 graphs in hardware experiments, with specific limitations in graph families and edge-weight distributions.

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