Recent Advances in the Calculation of Dynamical Correlation Functions

Abstract

We review various theoretical methods that have been used in recent years to calculate dynamical correlation functions of many-body systems. Time-dependent correlation functions and their associated frequency spectral densities are the quantities of interest, for they play a central role in both the theoretical and experimental understanding of dynamic properties. In particular, dynamic correlation functions appear in the fluctuation-dissipation theorem, where the response of a many-body system to an external perturbation is given in terms of the relaxation function of the unperturbed system, provided the disturbance is small. The calculation of the relaxation function is rather difficult in most cases of interest, except for a few examples where exact analytic expressions are allowed. For most of systems of interest approximation schemes must be used. The method of recurrence relation has, at its foundation, the solution of Heisenberg equation of motion of an operator in a many-body interacting system. Insights have been gained from theorems that were discovered with that method. For instance, the absence of pure exponential behavior for the relaxation functions of any Hamiltonian system. The method of recurrence relations was used in quantum systems such as dense electron gas, transverse Ising model, Heisenberg model, XY model, Heisenberg model with Dzyaloshinskii-Moriya interactions, as well as classical harmonic oscillator chains. Effects of disorder were considered in some of those systems. In the cases where analytical solutions were not feasible, approximation schemes were used, but are highly model-dependent. Another important approach is the numericallly exact diagonalizaton method. It is used in finite-sized systems, which sometimes provides very reliable information of the dynamics at the infinite-size limit. In this work, we discuss the most relevant applications of the method of recurrence relations and numerical calculations based on exact diagonalizations. The method of recurrence relations relies on the solution to the coefficients of a continued fraction for the Laplace transformed relaxation function. The calculation of those coefficients becomes very involved and, only a few cases offer exact solution. We shall concentrate our efforts on the cases where extrapolation schemes must be used to obtain solutions for long times (or low frequency) regimes. We also cover numerical work based on the exact diagonalization of finite sized systems. The numerical work provides some thermodynamically exact results and identifies some difficulties intrinsic to the method of recurrence relations.

Self-consistent relaxation theory of collective ion dynamics in Yukawa one-component plasmas under intermediate screening regimes

“…the set W(k) is solved in a self-consistent way as, for example, in the self-consistent mode-coupling theory [38][39][40][41][42], and no approximations of these time correlation functions by any model functions with free parameters are required [43]. This also becomes possible when the entire infinite set of sum rules (2) [or (3)] is known [31,36,[44][45][46]. In the case we consider here, the theoretical procedure is nonperturbative, which is especially appropriate for the description of the systems we are dealing with here.…”

Effects of trimodal random magnetic field on spin dynamics of quantum Ising chain

“…的最有效的方法之一 [34][35][36] 。该方法最初用来求解广义 Langevin 方 程，之后被逐渐用于研究自旋系统的动力学，例如多粒子系统、电子气、谐振子 链、简单流体等系统 [37] 。下面简述递推关系方法。…”

Improved Resource‐Tunable Near‐Term Quantum Algorithms for Transition Probabilities, with Applications in Physics and Variational Quantum Linear Algebra

“…These are required for calculating intensities in various areas of spectroscopy [ 6–11 ] and for response functions in scattering experiments and condensed matter. [ 12–15 ] Additionally, as vector‐matrix‐vector products $$\vec{a}^t {\bf A} \vec{b}$$ (or $$\vec{a}^t {\bf A} \vec{a}$$) are often relevant to classical linear algebra problems, transition probability subroutines may be useful in quantum linear algebra, [ 16–21 ] including for classical partial differential equations, [ 22–24 ] finance, [ 25,26 ] and quantum machine learning. [ 27–29 ]…”