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

P
Palo Alto Networks Blog
P
Proofpoint News Feed
GbyAI
GbyAI
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog
Google DeepMind News
Google DeepMind News
N
Netflix TechBlog - Medium
Recorded Future
Recorded Future
M
MIT News - Artificial intelligence
罗
罗磊的独立博客
J
Java Code Geeks
月光博客
月光博客
F
Full Disclosure
博
博客园 - 聂微东
人人都是产品经理
人人都是产品经理
U
Unit 42
WordPress大学
WordPress大学
A
About on SuperTechFans
C
Cyber Attacks, Cyber Crime and Cyber Security
SecWiki News
SecWiki News
Security Latest
Security Latest
C
Check Point Blog
C
CERT Recently Published Vulnerability Notes
小众软件
小众软件
I
InfoQ
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
B
Blog RSS Feed
V
Visual Studio Blog
博
博客园_首页
NISL@THU
NISL@THU
I
Intezer
Spread Privacy
Spread Privacy
AWS News Blog
AWS News Blog
The Register - Security
The Register - Security
D
Darknet – Hacking Tools, Hacker News & Cyber Security
Latest news
Latest news
Project Zero
Project Zero
博
博客园 - 叶小钗
C
Cybersecurity and Infrastructure Security Agency CISA
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
P
Privacy International News Feed
博
博客园 - 【当耐特】
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
T
Threat Research - Cisco Blogs
Simon Willison's Weblog
Simon Willison's Weblog
T
Tor Project blog
V
Vulnerabilities – Threatpost
博
博客园 - 司徒正美
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
雷峰网
雷峰网

stat updates on arXiv.org

Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad Shallow ReLU$^s$ Networks in $L^p$-Type and Sobolev Spaces: Approximation and Path-Norm Controlled Generalization On Stability and Decomposition of Sample Quantiles under Heavy-Tailed Distributions Improved Baselines with Representation Autoencoders Symmetry-Compatible Principle for Optimizer Design: Embeddings, LM Heads, SwiGLU MLPs, and MoE Routers Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent A Two-Parameter Weibull Framework for Diagnosing Transformer Weight Distributions Calibeating for general proper losses: A Bregman divergence approach Dimension-Free Convergence of Discrete Diffusion Models: Adjoint Equations Induce the Right Space Sample-efficient inductive matrix completion with noise and inexact side-information Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety XAI and Statistical Analysis for Reliable Intrusion Detection in the UAVIDS-2025 Dataset: From Tree to Hybrid and Tabular DNN Ensembles Reasoning Models Don't Just Think Longer, They Move Differently TabPFN-3: Technical Report Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models Towards a holistic understanding of Selection Bias for Causal Effect Identification Adaptive Kernel Density Estimation with Pre-training Coreset-Induced Conditional Velocity Flow Matching RISED: A Pre-Deployment Evaluation Framework for High-Stakes AI Decision-Support Systems, with Application to Healthcare ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks Yield Curves Dynamics Using Variational Autoencoders Under No-arbitrage Model-based Bootstrap of Controlled Markov Chains Online Learning-to-Defer with Varying Experts Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions One-Step Generative Modeling via Wasserstein Gradient Flows Exact Stiefel Optimization for Probabilistic PLS: Closed-Form Updates, Error Bounds, and Calibrated Uncertainty A Composite Activation Function for Learning Stable Binary Representations Adaptive Calibration in Non-Stationary Environments Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage On Variance Reduction in Learning Mean Flows When Attention Beats Fourier: Multi-Scale Transformers for PDE Solving on Irregular Domains A Refined Generalization Analysis for Extreme Multi-class Supervised Contrastive Representation Learning Ensemble Distributionally Robust Bayesian Optimisation The Proxy Presumption: From Semantic Embeddings to Valid Social Measures Modulated learning for private and distributed regression with just a single sample per client device Query-efficient model evaluation using cached responses Order-Agnostic Autoregressive Modelling with Missing Data Grokking or Glitching? How Low-Precision Drives Slingshot Loss Spikes Tuning Derivatives for Causal Fairness in Machine Learning Spherical Flows for Sampling Categorical Data Bayesian Rain Field Reconstruction using Commercial Microwave Links and Diffusion Model Priors Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning Jacobian-Velocity Bounds for Deployment Risk Under Covariate Drift Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics Graph Convolutional Support Vector Regression for Robust Spatiotemporal Forecasting of Urban Air Pollution Stochastic Schrödinger Diffusion Models for Pure-State Ensemble Generation Understanding Self-Supervised Learning via Latent Distribution Matching Imbalanced Classification under Capacity Constraints On the Spectral Structure and Objective Equivalence of Orthogonal Multilabel Fisher Discriminants Partially Observed Structural Causal Models Robust and Fast Training via Per-Sample Clipping Efficient Preference Poisoning Attack on Offline RLHF Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach Distributional Causal Mediation via Conditional Generative Modeling A Theory of Saddle Escape in Deep Nonlinear Networks Adaptive Querying with AI Persona Priors Optimal Spatio-Temporal Decoupling for Bayesian Conformal Prediction Wasserstein Distributionally Robust Regret Optimization for Reinforcement Learning from Human Feedback Electricity price forecasting across Norway's five bidding zones in the post-crisis era Adversarial Robustness of NTK Neural Networks Identifiability and Stability of Generative Drifting with Companion-Elliptic Kernel Families A Limit Theory of Foundation Models: A Mathematical Approach to Understanding Emergent Intelligence and Scaling Laws Conditional Score-Based Modeling of Effective Langevin Dynamics Inference of Online Newton Methods with Nesterov's Accelerated Sketching ProEval: Proactive Failure Discovery and Efficient Performance Estimation for Generative AI Evaluation Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces Learning to Emulate Chaos: Adversarial Optimal Transport Regularization Geometric Layer-wise Approximation Rates for Deep Networks S2MAM: Semi-supervised Meta Additive Model for Robust Estimation and Variable Selection Beyond Coefficients: Forecast-Necessity Testing for Interpretable Causal Discovery in Nonlinear Time-Series Models Curiosity-Critic: Cumulative Prediction Error Improvement as a Tractable Intrinsic Reward for World Model Training Knowing When to Quit: A Principled Framework for Dynamic Abstention in LLM Reasoning Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction Zeroth-Order Optimization at the Edge of Stability Generative Augmented Inference Estimating Continuous Treatment Effects with Two-Stage Kernel Ridge Regression Rare Event Analysis via Stochastic Optimal Control Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version Beyond Fixed False Discovery Rates: Post-Hoc Conformal Selection with E-Variables Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer Spatio-temporal probabilistic forecast using MMAF-guided learning Conformal Policy Control The Implicit Curriculum: Learning Dynamics in RL with Verifiable Rewards Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates Constrained Policy Optimization with Cantelli-Bounded Value-at-Risk Factorizable joint shift revisited Feature Learning Dynamics in Infinite-Depth Neural Networks Statistically-Guided Meta-Learning for Cross-Deployment Activity Recognition in Distributed Fiber-Optic Sensing DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions Neural ARFIMA model for forecasting BRIC exchange rates with long memory Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling BOOST: A Data-Driven Framework for the Automated Joint Selection of Kernel and Acquisition Functions in Bayesian Optimization Random Walk Learning and the Pac-Man Attack Random Matrix Theory for Deep Learning: Beyond Eigenvalues of Linear Models Post-Training Augmentation Invariance Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints Dataset-Driven Channel Masks in Transformers for Multivariate Time Series
Robust Structure Learning of $k$-local Lindbladians
[Submitted on 22 Jun 2026] · 2026-06-23 · via stat updates on arXiv.org

View PDF HTML (experimental)

Abstract:We present an efficient protocol for learning an unknown $k$-local Lindblad generator on $n$ qubits using only product-state preparations, short-time evolution, and single-qubit Pauli measurements, without prior knowledge of the interaction structure. For fixed $k$ and bounded weighted interaction strength, the protocol estimates all Hamiltonian and dissipative Pauli--GKSL coefficients to entrywise accuracy $\varepsilon$ with probability at least $1-\delta$ using $\widetilde{\mathcal O}_k(\varepsilon^{-2}n^{2k}\log(1/\delta))$ samples and polylogarithmically many evolution times. A semidefinite projection converts these estimates into a valid $k$-local Lindblad generator with diamond-norm error at most $\varepsilon$ using $\widetilde{\mathcal O}_k(\varepsilon^{-2}n^{4k}\log(1/\delta))$ samples and polynomial-time classical postprocessing. If a suitable set of influential coefficients is supplied and satisfies a stable sparsity condition, the dependence on $n$ can improve from polynomial to logarithmic; in particular, exact supports of bounded intersection degree require only $\widetilde{\mathcal O}_k(\varepsilon^{-2}\log(n/\delta))$ samples, with analogous reductions in system-size dependence for sufficiently decaying long-range interactions. We also provide a robust structure-learning procedure, extend the guarantees to model misspecification, and prove complementary sample-complexity lower bounds. To our knowledge, these are the first efficient learning guarantees for general $k$-local dissipative quantum dynamics under such limited experimental control.

Submission history

From: Tim Möbus [view email]
[v1] Mon, 22 Jun 2026 17:38:41 UTC (69 KB)

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。