Predicting open quantum dynamics with data-informed quantum-classical dynamics

TL;DR

This paper presents DIQCD, a data-informed quantum-classical method that accurately predicts the dynamics of open quantum systems using sparse data, applicable to quantum devices and materials.

Contribution

Introduction of DIQCD, a flexible, data-driven approach for modeling open quantum system dynamics with optimized, time-dependent Hamiltonians.

Findings

Accurately predicts entanglement in ultracold molecules.

Successfully models carrier mobility in organic semiconductors.

Demonstrates efficiency with experimental and simulated data.

Abstract

We introduce a data-informed quantum-classical dynamics (DIQCD) approach for predicting the evolution of an open quantum system. The equation of motion in DIQCD is a Lindblad equation with a flexible, time-dependent Hamiltonian that can be optimized to fit sparse and noisy data from local observations of an extensive open quantum system. We demonstrate the accuracy and efficiency of DIQCD for both experimental and simulated quantum devices. We show that DIQCD can predict entanglement dynamics of ultracold molecules (Calcium Fluoride) in optical tweezer arrays. DIQCD also successfully predicts carrier mobility in organic semiconductors (Rubrene) with accuracy comparable to nearly exact numerical methods.