Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations

TL;DR

This paper introduces a tensor-train thermo-field dynamics method to accurately compute memory kernels in generalized quantum master equations, enhancing quantum dynamics simulations and guiding quantum circuit development.

Contribution

It presents a novel tensor-train thermo-field dynamics approach to obtain exact memory kernels for GQME, improving understanding and accuracy in electronic dynamics modeling.

Findings

Exact memory kernels obtained from TT-TFD simulations.

Insights into sources of inaccuracies in GQME approaches.

Guidance for developing quantum circuits for GQME implementation.

Abstract

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.