Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering

2026-09-01

AI Summary

研究通过支配感知聚类方法解决量子误差缓解问题，提高从有限次电路执行中恢复高质量比特串的准确性。

Abstract

Many quantum algorithms for classically difficult optimization tasks must return high-quality bitstrings from finitely many circuit executions, whereas most quantum error-mitigation methods target expectation values. We study sample-level recovery when measured probability mass is distributed around multiple latent bitstrings, called centers. Each component of the measured probability mass is called a source and we assume that each center is associated with one source. We identify dominance-at every coordinate, more than half of a retained region's probability mass comes from one source and agrees with its center-as a sufficient condition under which majority voting recovers that center with exponentially decreasing error probability. We show that nearest-center assignment, as used in clustering algorithms such as the $k$-modes algorithm, can fail to produce dominated regions even when the true centers are known. This failure motivates responsibility thresholding and a local dominance screen, whose combination we call dominance-aware (DA) refinement. Synthetic and simulated MaxCut-QAOA experiments show that DA refinement favors precision, while $k$-modes with DA refinement improves overall center recovery. All procedures are classical post-processing and require no additional quantum-circuit executions.

Problem

• sample-level recovery
• quantum error mitigation
• probability mass distribution
• latent bitstrings
• dominance

Innovation

• dominance-aware refinement
• responsibility thresholding
• local dominance screen
• sample-level recovery
• quantum error mitigation