Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering
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.
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BibTeX
@misc{ghodrati2026reliable,
title = {{Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering}},
author = {Mohsen Ghodrati and Kausthubh Chandramouli and Dror Baron},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01744},
eprint = {2609.01744},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01744},
}
RIS
TY - UNPB
TI - Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering
AU - Mohsen Ghodrati
AU - Kausthubh Chandramouli
AU - Dror Baron
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01744
UR - https://arxiv.org/abs/2609.01744
AB - 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.
ER -