论文标题
张量训练训练热场记忆内核,用于广义量子主方程
Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations
论文作者
论文摘要
广义量子主方程(GQME)方法提供了一个严格的框架,用于为任何电子还原密度矩阵元素(例如,对角线元素)的任何子集进行精确运动方程。在电子动力学的背景下,GQME的记忆内核和不均匀项引入了与核运动或电子密度矩阵元素的动力学的隐式耦合(例如,非基因配元素),从而允许进行有效的量子动力学模拟。在这里,我们专注于通过各种类型的GQME描述的自旋玻色子模型系统中电子动力学的基准量子模拟。精确的存储内核和不均匀项是从短时量子力学精确量量量热场动力学(TT-TFD)模拟中获得的。当与近似输入方法结合使用时,TT-TFD内存内核提供了有关GQME方法不准确的主要来源的见解,并为开发可以在数字量子计算机上实现GQME的量子电路的道路铺平了道路。
The generalized quantum master equation (GQME) approach provides a rigorous framework for deriving the exact equation of motion for any subset of electronic reduced density matrix elements (e.g., the diagonal elements). In the context of electronic dynamics, the memory kernel and inhomogeneous term of the GQME introduce the implicit coupling to nuclear motion or dynamics of electronic density matrix elements that are projected out (e.g., the off-diagonal elements), allowing for efficient quantum dynamics simulations. Here, we focus on benchmark quantum simulations of electronic dynamics in a spin-boson model system described by various types of GQMEs. Exact memory kernels and inhomogeneous terms are obtained from short-time quantum-mechanically exact tensor-train thermo-field dynamics (TT-TFD) simulations. The TT-TFD memory kernels provide insights on the main sources of inaccuracies of GQME approaches when combined with approximate input methods and pave the road for development of quantum circuits that could implement GQMEs on digital quantum computers.