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

爱范儿
爱范儿
WordPress大学
WordPress大学
博客园 - 【当耐特】
The Cloudflare Blog
B
Blog
Last Week in AI
Last Week in AI
小众软件
小众软件
量子位
S
SegmentFault 最新的问题
V
Visual Studio Blog
博客园 - 叶小钗
美团技术团队
阮一峰的网络日志
阮一峰的网络日志
Hugging Face - Blog
Hugging Face - Blog
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
宝玉的分享
宝玉的分享
A
About on SuperTechFans
雷峰网
雷峰网
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
腾讯CDC
MongoDB | Blog
MongoDB | Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Martin Fowler
Martin Fowler

cs.DS updates on arXiv.org

PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
A doubly exponential upper bound on noisy EPR states for ...
Penghui Yao · 2019-04-18 · via cs.DS updates on arXiv.org

This paper initiates the study of a class of entangled games, mono-state games, denoted by $(G,ψ)$, where $G$ is a two-player one-round game and $ψ$ is a bipartite state independent of the game $G$. In the mono-state game $(G,ψ)$, the players are only allowed to share arbitrary copies of $ψ$. This paper provides a doubly exponential upper bound on the copies of $ψ$ for the players to approximate the value of the game to an arbitrarily small constant precision for any mono-state binary game $(G,ψ)$, if $ψ$ is a noisy EPR state, which is a two-qubit state with completely mixed states as marginals and maximal correlation less than $1$. In particular, it includes $(1-ε)|Ψ\rangle\langleΨ|+ε\frac{I_2}{2}\otimes\frac{I_2}{2}$, an EPR state with an arbitrary depolarizing noise $ε>0$.The structure of the proofs is built the recent framework about the decidability of the non-interactive simulation of joint distributions, which is completely different from all previous optimization-based approaches or "Tsirelson's problem"-based approaches. This paper develops a series of new techniques about the Fourier analysis on matrix spaces and proves a quantum invariance principle and a hypercontractive inequality of random operators. This novel approach provides a new angle to study the decidability of the complexity class MIP$^*$, a longstanding open problem in quantum complexity theory.