1 research outputs found
Tensor-Train Thermo-Field Memory Kernels for Generalized Quantum Master Equations
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 and 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 and are compared with those
obtained from an approximate linearized semiclassical method, allowing
for assessment of the accuracy of these approximate memory kernels
and inhomogeneous terms. Moreover, we have analyzed the computational
cost of the full and reduced-dimensionality GQMEs. The scaling of
the computational cost is dependent on several factors, sometimes
with opposite scaling trends. The TT-TFD memory kernels can provide
insights on the main sources of inaccuracies of GQME approaches when
combined with approximate input methods and pave the road for the
development of quantum circuits that implement GQMEs on digital quantum
computers