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arXiv 2102.04146quant-phmath.OA
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量子马氏链的完全熵不等式

Complete entropic inequalities for quantum Markov chains

Li Gao, Cambyse Rouzé

摘要:

证明了有限维矩阵代数上的高斯对称量子马尔可夫半群满足修正的log-Sobolev不等式。在离散时间条件下,我们证明了有限维高斯对称量子信道关于其无退相干部分满足一个强数据处理不等式。此外,我们建立了相对熵的第一广义张量化近似性质。这将著名的强次可加性的量子熵(SSA)的两个子系统的一般设置的两个子代数。所有这三个结果都与环境的大小无关,因此满足张量化性质。它们是通过一个共同的,概念上简单的方法证明熵不等式通过频谱或$L_2$ -估计。作为应用,我们结合我们的修改后的log-Sobolev不等式和近似张量化的结果,推导出的理论和实际相关的例子,包括表示子拉普拉斯算子的$\operatorname{SU}(2)$和各种类的本地量子马尔可夫半群,如量子Kac生成器和连续时间近似酉设计的界限。对于后者,我们的界限意味着存在局部连续时间马尔可夫演化的$nk$ qudits形成$ε$ -近似$k$ -设计的相对熵为$\widetilde{\mathcal{O}}(n^2 \operatorname{poly}(k))$的时间尺度。

英文摘要:

We prove that every GNS-symmetric quantum Markov semigroup on a finite dimensional matrix algebra satisfies a modified log-Sobolev inequality. In the discrete time setting, we prove that every finite dimensional GNS-symmetric quantum channel satisfies a strong data processing inequality with respect to its decoherence free part. Moreover, we establish the first general approximate tensorization property of relative entropy. This extends the famous strong subadditivity of the quantum entropy (SSA) of two subsystems to the general setting of two subalgebras. All the three results are independent of the size of the environment and hence satisfy the tensorization property. They are obtained via a common, conceptually simple method for proving entropic inequalities via spectral or $L_2$-estimates. As applications, we combine our results on the modified log-Sobolev inequality and approximate tensorization to derive bounds for examples of both theoretical and practical relevance, including representation of sub-Laplacians on $\operatorname{SU}(2)$ and various classes of local quantum Markov semigroups such as quantum Kac generators and continuous time approximate unitary designs. For the latter, our bounds imply the existence of local continuous time Markovian evolutions on $nk$ qudits forming $ε$-approximate $k$-designs in relative entropy for times scaling as $\widetilde{\mathcal{O}}(n^2 \operatorname{poly}(k))$.

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