惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园_首页
N
Netflix TechBlog - Medium
V
Visual Studio Blog
博客园 - Franky
小众软件
小众软件
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 三生石上(FineUI控件)
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
宝玉的分享
宝玉的分享
量子位
大猫的无限游戏
大猫的无限游戏
人人都是产品经理
人人都是产品经理
V
V2EX
The Cloudflare Blog
月光博客
月光博客
Last Week in AI
Last Week in AI
雷峰网
雷峰网
WordPress大学
WordPress大学
博客园 - 【当耐特】
博客园 - 聂微东
IT之家
IT之家
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻

math updates on arXiv.org

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization Non-normal spectral signatures of instability in neural network training dynamics Optimization of randomized neural networks for transfer operator approximation Selective Ambulance Dispatch Under Contextual Travel-Time Uncertainty LLAMA LIMA: A Living Meta-Analysis on the Effects of Generative AI on Learning Mathematics Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations LLMs as Noisy Channels: A Shannon Perspective on Model Capacity and Scaling Laws On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy Training-Free Looped Transformers Move on Muon : A Hamiltonian probability gradient flow perspective of Muon optimizer Entrywise Error Bounds for Spectral Ranking with Semi-Random Adversaries Asymmetric Scaling Laws from Sparse Features Is Dimensionality a Barrier for Retrieval Models? RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs Commutator-Induced Uncertainty in VAEs Weisfeiler-Leman Is Incomplete on Simple Spectrum Graphs, so Canonicalize Them Sparse In-Network Learning via Shortest-Path Backpropagation and Finite-Rate Gating Instance-Optimal Estimation with Multiple LLM Judges on a Budget Entropy Equivalence Testing Expand More, Shrink Less: Shaping Effective-Rank Dynamics for Dense Scaling in Recommendation Any-Dimensional Invariant Universality Operationalizing Individual Fairness via Gradient Descent and Bradley-Terry Models Anytime Training with Schedule-Free Spectral Optimization Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology The General Theory of Localization Methods Group-Algebraic Tensors: Provably-optimal Equivariant Learning and Physical Symmetry Discovery General Lower Bounds for Differentially Private Federated Learning with Arbitrary Public-Transcript Interactions PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels Proximal basin hopping: global optimization with guarantees
A Posterior MWPM Decoding Boosts the XYZ Planar Code
Zhiwei Wang, Liqi Wang · 2026-05-22 · via math updates on arXiv.org

The minimum-weight perfect matching (MWPM) decoder is a standard decoding strategy for surface codes, but its performance degrades considerably under biased noise. In this paper, a modified surface code, termed the XYZ planar code, is introduced, and the MWPM decoder is extended to posterior MWPM (pMWPM) with almost no increase in decoding complexity. The XYZ planar code exhibits higher and more stable thresholds than the planar code under almost all bias conditions, while also achieving significantly lower logical error rates. Specifically, in the infinite-bias case, the threshold of the XYZ planar code is improved by about \(36\%\) compared to that of the surface code, and it maintains comparable or higher thresholds under other biases -- for example, the threshold reaches approximately \(15.5\%\) at bias \(η= 1\) and \(14.2\%\) at \(η= 100\). Furthermore, pMWPM can be adapted to a wide range of modified surface codes, and the results presented in this work also indicate its excellent potential in other scenarios, such as configurations in which \(Y\) operators involve a larger number of data qubits.