analiza aiqbench
This study introduces ECCentric, the first comprehensive benchmarking framework for evaluating quantum error correction codes across realistic hardware constraints and noise models. The findings highlight that qubit connectivity outweighs code distance in reducing logical errors and identify trapped-ion architectures with shuttling as a leading candidate for near-term fault tolerance. These empirical insights provide critical guidance for optimizing compiler strategies and selecting appropriate QEC schemes before full-scale quantum advantage is achieved.
ECCentric: An Empirical Analysis of Quantum Error Correction Codes
arXiv:2511.01062v2 Announce Type: replace Abstract: Quantum Error Correction (QEC) is essential for building scalable quantum computers, but a lack of systematic, end-to-end evaluation methods makes it difficult to assess how different QEC codes perform under realistic conditions. The vast diversity of codes, an expansive experimental search space, and the absence of a standardized framework prevent a thorough, holistic analysis. To address this, we introduce ECCentric, an end-to-end benchmarking framework designed to systematically evaluate QEC codes across the full quantum computing stack. ECCentric is designed to be modular, extensible, and general, allowing for a comprehensive analysis of QEC code families under varying hardware topologies, noise models, and compilation strategies. Using ECCentric, we conduct the first systematic benchmarking of major QEC code families against realistic, mid-term quantum device parameters. Our empirical analysis reveals that intra-QPU execution significantly outperforms distributed methods, that qubit connectivity is a far more critical factor for reducing logical errors than increasing code distance, and that compiler overhead remains a major source of error. Furthermore, our findings suggest that trapped-ion architectures with qubit shuttling are the most promising near-term platforms and that on noisy devices, a strategic and selective application of QEC is necessary to avoid introducing more errors than are corrected. This study provides crucial, actionable insights for both hardware designers and practitioners, guiding the development of fault-tolerant quantum systems.
Abstract:Quantum Error Correction (QEC) is essential for building scalable quantum computers, but a lack of systematic, end-to-end evaluation methods makes it difficult to assess how different QEC codes perform under realistic conditions. The vast diversity of codes, an expansive experimental search space, and the absence of a standardized framework prevent a thorough, holistic analysis. To address this, we introduce ECCentric, an end-to-end benchmarking framework designed to systematically evaluate QEC codes across the full quantum computing stack. ECCentric is designed to be modular, extensible, and general, allowing for a comprehensive analysis of QEC code families under varying hardware topologies, noise models, and compilation strategies. Using ECCentric, we conduct the first systematic benchmarking of major QEC code families against realistic, mid-term quantum device parameters. Our empirical analysis reveals that intra-QPU execution significantly outperforms distributed methods, that qubit connectivity is a far more critical factor for reducing logical errors than increasing code distance, and that compiler overhead remains a major source of error. Furthermore, our findings suggest that trapped-ion architectures with qubit shuttling are the most promising near-term platforms and that on noisy devices, a strategic and selective application of QEC is necessary to avoid introducing more errors than are corrected. This study provides crucial, actionable insights for both hardware designers and practitioners, guiding the development of fault-tolerant quantum systems.
arXiv:2511.01062 [quant-ph]
(or arXiv:2511.01062v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2511.01062 Focus to learn more arXiv-issued DOI via DataCite
Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 10, Issue 2, Article 37 (2026), pp. 1-33
https://doi.org/10.1145/3805635 Focus to learn more DOI(s) linking to related resources
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