Neural Quantum Propagators for Driven-Dissipative Quantum Dynamics

TL;DR

This paper introduces driven neural quantum propagators (NQP), a neural network framework that efficiently simulates driven-dissipative quantum dynamics by approximating propagators, enabling long-time and transferable simulations across different systems.

Contribution

The work develops a universal neural network approach that approximates quantum propagators, allowing for flexible, long-time, and transferable simulations of open quantum systems.

Findings

NQP can simulate long-time quantum dynamics from short training windows.

NQP is adaptable to various external fields and initial states.

NQP demonstrates effectiveness on spin-boson and three-state transition models.

Abstract

Describing the dynamics of strong-laser driven open quantum systems is a very challenging task that requires the solution of highly involved equations of motion. While machine learning techniques are being applied with some success to simulate the time evolution of individual quantum states, their use to approximate time-dependent operators (that can evolve various states) remains largely unexplored. In this work, we develop driven neural quantum propagators (NQP), a universal neural network framework that solves driven-dissipative quantum dynamics by approximating propagators rather than wavefunctions or density matrices. NQP can handle arbitrary initial quantum states, adapt to various external fields, and simulate long-time dynamics, even when trained on far shorter time windows. Furthermore, by appropriately configuring the external fields, our trained NQP can be transferred to systems governed by different Hamiltonians. We demonstrate the effectiveness of our approach by studying the spin-boson and the three-state transition Gamma models.