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Paper 1

Decoder Performance in Hybrid CV-Discrete Surface-Code Threshold Estimation Using LiDMaS+

Dennis Delali Kwesi Wayo, Chinonso Onah, Vladimir Milchakov, Leonardo Goliatt, Sven Groppe

Year

2026

Journal

arXiv preprint

DOI

arXiv:2603.06730

arXiv

2603.06730

Threshold estimation is central to fault-tolerant quantum computing, but the reported threshold depends not only on the code and noise model, but also on the decoder used to interpret syndrome data. We study this dependence for surface-code threshold estimation under both a standard Pauli noise model and a hybrid continuous-variable/discrete model motivated by GKP-style digitization. Using LiDMaS+ as a common experimental platform, we compare minimum-weight perfect matching (MWPM) and Union-Find under matched sweep grids, matched distances, and deterministic seeding, and we additionally evaluate trained neural-guided MWPM in the hybrid regime. In the Pauli baseline at distance $d=5$, MWPM consistently outperforms Union-Find, reducing the mean sampled logical error rate from $0.384$ to $0.260$, and producing a stable threshold summary with crossing median $p_c \approx 0.053$. In the hybrid fixed-distance run, Union-Find is substantially worse than MWPM (mean LER $0.1657$ versus $0.1195$), while trained neural-guided MWPM tracks MWPM closely (mean LER $0.1158$). Across hybrid multi-distance sweeps, the distance-dependent reversal in logical-error ordering remains visible, but the grid-based crossing estimator still returns boundary-valued $σ_c=0.05$ for all decoders. Neural-guided runs also show elevated decoder-failure diagnostics at high noise ($\max$ decoder-failure rate $0.1335$ at $d=7,σ=0.60$), indicating that learned guidance quality and decoder robustness must be reported alongside threshold curves. These results show that decoder choice and estimator design both materially affect threshold inference.

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Paper 2

Synergy between Charge Transfer and Spatial Descriptors in Determining the Band Gap of hP4-Na: An Interpretable Machine Learning Approach.

Zhang L, Wei Y, Yan X, Yang B

Year

2026

Journal

Inorganic chemistry

DOI

10.1021/acs.inorgchem.6c00324

arXiv

-

Electrides are a class of materials whose highly localized electrons in the lattice interstices exhibit anion-like behavior, known as interstitial quasi-atoms (ISQs). Nonmetallic electrides are promising for extreme-environment optical and sensing applications due to their pressure-retained band gaps and tunable electronic structures, yet the microscopic mechanisms governing their band gap remain unclear. Using the high-pressure phase hP4-Na, this study reveals these mechanisms through first-principles calculations under pressure and strain, combined with machine learning. Interpretability analysis identifies charge transfer () and electron spatial distribution () as dominant factors modulating the band gap. Through symbolic regression, we derive a concise analytical formula based solely on five electronic-structure descriptors, achieving excellent predictive accuracy ( > 0.98) against first-principles results. This directly confirms the electronic structure as the physical origin of the band gap in hP4-Na. Unlike previous studies focused on electron localization function, we show that is a key descriptor linking seemingly disparate properties like insulating behavior and superconductivity. Our study provides a microscopic understanding and a quantitative predictive framework for hP4-Na's electronic behavior under complex stress, establishing a foundation for rational design of high-pressure electrides.

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