TBNQuantum
Scaling Laws for Parameter Setting in Digitized Counterdiabatic QAOA
Publication: quant-ph updates on arXiv.org
Author: Yaocheng Chen, Jiapei Zhuang
Source published: 03/11/2026, 05:00:00 UTC
Briefing created: 03/11/2026, 09:02:54 UTC
Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 03/11/2026, 08:58:25 UTC.
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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