论文标题
非马克维亚动态的紧凑而完整的描述
Compact and complete description of non-Markovian dynamics
论文作者
论文摘要
广义的主方程提供了一个理论上严格的框架,以捕获从植物和光伏设备中的能量收集到量子技术中的Qubit反应,甚至蛋白质折叠的过程。在他们的中心是记忆的概念。记忆的显式时间非定位描述既持久又精心制作。当物理直觉处于溢价时,人们会想要更紧凑,但完整的描述。在这里,我们演示了如何以及何时无限距离的形式主义构成这样的描述。特别是,通过关注自旋 - 玻色子和Frenkel激子模型的耗散动力学,我们展示了:轻松地从参考减少的动力学中构建时间局部生成器,阐明其存在对系统参数的依赖性以及对降低的可观察到的可观察的选择,并确定其明显的差异工具,并诊断出其明显的差异和造成的效果和效果效率,并构成其效果的效果和效果。我们证明,在适用的情况下,时间本地方法所需的信息与更常用的时间非定位方案一样少,具有提供更紧凑的描述,更大的算法简单性和物理解释性的重要优势。在参考动力学有限的情况下,我们通过引入离散时间模拟和直接的协议来结束。我们在这里提供的见解提供了扩展动力学方法的范围,降低其成本和概念复杂性的潜力。
Generalized master equations provide a theoretically rigorous framework to capture the dynamics of processes ranging from energy harvesting in plants and photovoltaic devices, to qubit decoherence in quantum technologies, and even protein folding. At their center is the concept of memory. The explicit time-nonlocal description of memory is both protracted and elaborate. When physical intuition is at a premium one would desire a more compact, yet complete, description. Here, we demonstrate how and when the time-convolutionless formalism constitutes such a description. In particular, by focusing on the dissipative dynamics of the spin-boson and Frenkel exciton models, we show how to: easily construct the time-local generator from reference reduced dynamics, elucidate the dependence of its existence on the system parameters and the choice of reduced observables, identify the physical origin of its apparent divergences, and offer analysis tools to diagnose their severity and circumvent their deleterious effects. We demonstrate that, when applicable, the time-local approach requires as little information as the more commonly used time-nonlocal scheme, with the important advantages of providing a more compact description, greater algorithmic simplicity, and physical interpretability. We conclude by introducing the discrete-time analogue and a straightforward protocol to employ it in cases where the reference dynamics have limited resolution. The insights we present here offer the potential for extending the reach of dynamical methods, reducing both their cost and conceptual complexity.