论文标题
如何以时间本地的方式生成记忆的量子演变
How quantum evolution with memory is generated in a time-local way
论文作者
论文摘要
开放量子系统动力学的两种广泛使用但独特的方法分别是Nakajima-Zwanzig和时间符号的无量子主方程。尽管两者都描述了具有强烈内存效果的相同量子演变,但第一个使用时间非局部内存内核$ \ MATHCAL {K} $,而第二个则使用时机发电机$ \ MATHCAL {g} $实现了相同的成就。在这里,我们表明这两者是通过简单但一般的定点关系连接的:$ \ MATHCAL {g} = \ hat {\ Mathcal {k}} [\ Mathcal {g}] $。这使一个人可以在计算时间进化并结合优势的两种完全不同的方式之间提取非平凡关系。我们首先讨论固定发电机,该发生器可以实现Markov近似,该近似既是非扰动性的,又对大量的演变完全积极。我们表明,该发电机不等于内存内核的低频极限,而是在非零特性频率下“样本”。这阐明了现有的马尔可夫近似策略中频率依赖性和半群分分解的微妙作用。其次,我们证明,定点方程将用于时间非量子量子主方程的时间域梯度 / Moyal扩展总结,从而对内存效应的产生提供了非扰动的见解。最后,我们表明,定点关系可以从给定的内存内核对固定和瞬态发生器进行直接迭代数值计算。对于瞬态发生器,这会产生非隔离近似值,这些近似在每个迭代步骤中都被约束为最初和渐近准确。
Two widely used but distinct approaches to the dynamics of open quantum systems are the Nakajima-Zwanzig and time-convolutionless quantum master equation, respectively. Although both describe identical quantum evolutions with strong memory effects, the first uses a time-nonlocal memory kernel $\mathcal{K}$, whereas the second achieves the same using a time-local generator $\mathcal{G}$. Here we show that the two are connected by a simple yet general fixed-point relation: $\mathcal{G} = \hat{\mathcal{K}}[\mathcal{G}]$. This allows one to extract nontrivial relations between the two completely different ways of computing the time-evolution and combine their strengths. We first discuss the stationary generator, which enables a Markov approximation that is both nonperturbative and completely positive for a large class of evolutions. We show that this generator is not equal to the low-frequency limit of the memory kernel, but additionally "samples" it at nonzero characteristic frequencies. This clarifies the subtle roles of frequency dependence and semigroup factorization in existing Markov approximation strategies. Second, we prove that the fixed-point equation sums up the time-domain gradient / Moyal expansion for the time-nonlocal quantum master equation, providing nonperturbative insight into the generation of memory effects. Finally, we show that the fixed-point relation enables a direct iterative numerical computation of both the stationary and the transient generator from a given memory kernel. For the transient generator this produces non-semigroup approximations which are constrained to be both initially and asymptotically accurate at each iteration step.