University of Florida Homepage

Victor S. Batista

Tensor train algorithms for quantum dynamics simulations of excited state nonadiabatic processes and quantum control

Victor S. Batista

Yale University, Department of Chemistry & Energy Sciences Institute

We introduce the “tensor-train split-operator Fourier transform” (TT-SOFT) algorithm for simulations of multidimensional nonadiabatic quantum dynamics. TT-SOFT is essentially the grid-based SOFT method implemented in dynamically adaptive tensor-train representations. In the same spirit of all matrix product states, the tensor-train format enables the representation, propagation, and computation of observables of multidimensional wave functions in terms of the grid-based wavepacket tensor components, bypassing the need of actually computing the wave function in its full-rank tensor product grid space. We demonstrate the accuracy and efficiency of the TT-SOFT method as applied to propagation of 24-dimensional wave packets, describing the S1/S2 interconversion dynamics of pyrazine after UV photoexcitation to the S2 state. Our results show that the TT-SOFT method is a powerful computational approach for simulations of quantum dynamics of polyatomic systems since it avoids the exponential scaling problem of full rank grid-based representations [J. Chem. Theory Comput. 13:4034-4042 (2017)]. We also present an accurate description of nonadiabatic energy relaxation crucial for modeling atomistic dynamics at metallic surfaces [Phys. Rev. Lett. 116, 217601 (2016)] and the description of robust memristors (resistive memory films) for neuromorphic computing based on solution-processable metal-coordinated azo aromatics [Nature Materials 16: 1216-1224 (2017)].