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Paper 1
Geometry-induced correlated noise in qLDPC syndrome extraction
Angelo Di Bella
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2604.01040
- arXiv
- 2604.01040
With code and syndrome-extraction schedule fixed, can routed geometry alone change the correlated fault model enough to impact logical performance? Starting from a geometry-conditioned same-tick interaction Hamiltonian, we derive a controlled retained single-and-pair data-fault model for bivariate-bicycle (BB) layouts. Two geometry metrics emerge in two kernel regimes: under a crossing-local diagnostic kernel, a matching argument reduces the support-level effective fault weight; when every support pair appears in at least one retained round with finite same-round separation, strictly positive kernels saturate the support graph, and weighted exposure becomes the discriminating quantity. Circuit-level Monte Carlo on the $[\![72, 12, 6]\!]$ and $[\![144, 12, 12]\!]$ benchmarks confirms that a biplanar bounded-thickness layout suppresses the monomial single-layer embedding penalty, with weighted exposure tracking logical error rate across 101 operating points (Spearman correlation 0.893). A single-layer logical-family optimization on BB72 reduces worst-case exposure by 26.11% and lowers logical error rate in the tested power-law window. Routed geometry should be optimized together with code, schedule, and decoder.
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Decoder Dependence in Surface-Code Threshold Estimation with Native Gottesman-Kitaev-Preskill Digitization and Parallelized Sampling
Dennis Delali Kwesi Wayo, Chinonso Onah, Leonardo Goliatt, Sven Groppe
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.25757
- arXiv
- 2603.25757
We quantify decoder dependence in surface-code threshold studies under two matched regimes: Pauli noise and native GKP-style Gaussian displacement digitization. Using LiDMaS+ v1.1.0, we benchmark MWPM, Union-Find (UF), Belief Propagation (BP), and neural-guided MWPM with fixed seeds, identical sweep grids, and unified reporting across runs 06--14. At $d=5$ and $σ=0.20$, MWPM and UF define the Pareto frontier, with (runtime, LER) = (1.341 s, 0.2273) and (1.332 s, 0.2303); neural-guided MWPM is slower and less accurate (1.396 s, 0.3730), and BP is dominated (7.640 s, 0.6107). Crossing-bootstrap diagnostics are stable only for MWPM, with median $σ^\star_{3,5}=0.10$ (1911/2000 valid) and $σ^\star_{5,7}=0.1375$ (1941/2000 valid), while other decoders show no valid crossing samples. Dense-window scanning over $σ\in [0.08,0.24]$ returns NaN crossings for all decoders, confirming estimator- and window-sensitive threshold localization. Rank-stability and effect-size bootstrap analyses reinforce ordering robustness: BP remains rank 4, neural-guided MWPM rank 3, and MWPM-UF differences are small ($Δ_{\mathrm{MWPM-UF}}=-0.00383$, 95\% interval $[-0.0104,0.00329]$) across $σ\in [0.05,0.35]$. Threaded execution preserves statistical fidelity while improving throughput: $1.34\times$ speedup in Pauli mode and $1.94\times$ in native GKP mode, with mean $|Δ\mathrm{LER}|$ $6.07\times10^{-3}$ and $5.20\times10^{-3}$, respectively. We therefore recommend estimator-conditional threshold reporting coupled to runtime-fidelity checks for reproducible hardware-facing practical future decoder benchmarking workflows.
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