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Paper 1
Correlated Atom Loss as a Resource for Quantum Error Correction
Hugo Perrin, Gatien Roger, Guido Pupillo
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.24237
- arXiv
- 2603.24237
Atom loss is a dominant error source in neutral-atom quantum processors, yet its correlated structure remains largely unexploited by existing quantum error correction decoders. We analyze the performance of the surface code equipped with teleportation-based loss-detection units for neutral-atom quantum processors subject to circuit-level, partially correlated atom loss and depolarizing noise. We introduce and implement a decoding strategy that exploits loss correlations, effectively converting the \textit{delayed} erasure channels stemming from atom loss to erasure channels. The decoder constructs a loss graph and dynamically updates loss probabilities, a procedure that is highly parallelizable and compatible with real-time operation. Compared to a decoder that assumes independent loss events, our approach achieves up to an order-of-magnitude reduction in logical error probability and increases the loss threshold from $3.2\%$ to $4\%$. Our approach extends to experimentally relevant regimes with partially correlated loss, demonstrating robust gains beyond the idealized fully correlated setting.
Open paperPaper 2
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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