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This study validates the critical interface between hardware syndrome measurements and decoder logic across multiple quantum error-correction schemes, including repetition codes, surface codes, CSS-LDPC benchmarks, and digitized-GKP models. By comparing IBM gate-model circuits with a PennyLane simulation, the research quantifies how noise impacts exact localization versus target-containing detection without making premature threshold claims. These findings provide essential empirical data for engineers building robust fault-tolerant quantum processors where reliable syndrome-to-decoder communication is paramount.
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Hardware-in-the-Loop Syndrome-to-Decoder Validation for Repetition, Surface, CSS-LDPC, and Digitized-GKP Codes

arXiv3 min read

arXiv:2607.19447v1 Announce Type: new Abstract: Quantum error-correction experiments increasingly require a verified interface between measured syndrome bits and decoder-native correction requests. We report a four-branch syndrome-to-decoder study spanning three IBM gate-model hardware circuits and one PennyLane-backed digitized-GKP model. The hardware branches implement a five-data-qubit repetition code, a distance-five rotated-surface-code Z-check extraction layer, and the Z-check half of the Steane CSS code as a compact CSS-LDPC benchmark. The GKP branch samples finite-squeezed Gaussian-CV q-readout and injected q-shifts, then bins wrapped quadrature coordinates into the same outer surface-code Z-check interface. All cases use 4096 shots per stream, clean and injected streams, LiDMaS+ request construction, and MWPM/minimum-weight correction as the plotted baseline, with union-find and hard-decision belief-propagation/min-sum policies replayed for interface validation. The correction-volume panels additionally report mean minimum-weight correction weight for each decoded stream. Repetition and CSS-LDPC hardware preserve the dominant expected syndrome and correction for every injected target. The routed 56-qubit surface circuit exhibits broad hardware-induced syndrome activation: exact localization drops to $0.003$--$0.108$, but target-containing localization remains $0.279$--$0.642$. The digitized-GKP study gives exact q-shift localization of $0.350$--$0.495$ and target-containing localization of $0.417$--$0.608$. The results support an auditable syndrome-to-decoder interface rather than a threshold claim.

Abstract:Quantum error-correction experiments increasingly require a verified interface between measured syndrome bits and decoder-native correction requests. We report a four-branch syndrome-to-decoder study spanning three IBM gate-model hardware circuits and one PennyLane-backed digitized-GKP model. The hardware branches implement a five-data-qubit repetition code, a distance-five rotated-surface-code Z-check extraction layer, and the Z-check half of the Steane CSS code as a compact CSS-LDPC benchmark. The GKP branch samples finite-squeezed Gaussian-CV q-readout and injected q-shifts, then bins wrapped quadrature coordinates into the same outer surface-code Z-check interface. All cases use 4096 shots per stream, clean and injected streams, LiDMaS+ request construction, and MWPM/minimum-weight correction as the plotted baseline, with union-find and hard-decision belief-propagation/min-sum policies replayed for interface validation. The correction-volume panels additionally report mean minimum-weight correction weight for each decoded stream. Repetition and CSS-LDPC hardware preserve the dominant expected syndrome and correction for every injected target. The routed 56-qubit surface circuit exhibits broad hardware-induced syndrome activation: exact localization drops to $0.003$--$0.108$, but target-containing localization remains $0.279$--$0.642$. The digitized-GKP study gives exact q-shift localization of $0.350$--$0.495$ and target-containing localization of $0.417$--$0.608$. The results support an auditable syndrome-to-decoder interface rather than a threshold claim.

arXiv:2607.19447 [quant-ph]

(or arXiv:2607.19447v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2607.19447 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 279 words

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