4.6 Article

Correlations in typicality and an affirmative solution to the exact catalytic entropy conjecture

期刊

QUANTUM
卷 6, 期 -, 页码 -

出版社

VEREIN FORDERUNG OPEN ACCESS PUBLIZIERENS QUANTENWISSENSCHAF
DOI: 10.48550/arXiv.2205.08915

关键词

-

资金

  1. DFG [EXC-2123]
  2. Quantum Valley Lower Saxony
  3. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germanys Excellence Strategy QuantumFrontiers [390837967]
  4. [SFB 1227]

向作者/读者索取更多资源

This short note discusses a property of quantum states and density matrices. It states that if a density matrix ρ has a smaller entropy than ρ0, then the tensor product of sufficiently many copies of ρ can approximate a quantum state that is very close to the tensor product of the corresponding number of copies of ρ0. Additionally, if the rank of ρ is smaller than the rank of ρ0, then the tensor product of n copies of ρ can dominate a state with identical single-body marginals as ρ0 but allowing for arbitrary correlations.
It is well known that if a (finite-dimensional) density matrix rho has smaller entropy than rho 0, then the tensor product of sufficiently many copies of rho majorizes a quantum state arbitrarily close to the tensor product of correspondingly many copies of rho 0. In this short note I show that if additionally rank(rho) < rank(rho 0), then n copies of rho also majorize a state where all single-body marginals are exactly identical to rho 0 but arbitrary correlations are allowed (for some sufficiently large n). An immediate application of this is an affirmative solution of the exact catalytic entropy conjecture introduced by Boes et al. [PRL 122, 210402 (2019)]: If H(rho) < H(rho 0) and rank(rho) < rank(rho 0) there exists a finite dimensional density matrix sigma and a unitary U such that U rho (R) sigma U & DAG; has marginals rho 0 and sigma exactly. All the results transfer to the classical setting of probability distributions over finite alphabets with unitaries replaced by permutations.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据