Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks

TL;DR

This paper presents a neural network approach using recurrent neural networks to model complex non-Markovian quantum processes, enabling accurate quantum process tomography across various regimes.

Contribution

It introduces a novel neural network framework that captures non-Markovian effects in quantum dynamics, extending the modeling capabilities beyond traditional methods.

Findings

Recurrent neural networks effectively model non-Markovian quantum processes.

The approach accurately reproduces quantum evolution from initial states.

Neural networks outperform traditional methods in quantum process tomography.

Abstract

Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce a neural network based approach, which has the mathematical simplicity of the Gorini-Kossakowski-Sudarshan-Lindblad master equation, but is able to model non-Markovian effects in different regimes. This is achieved by using recurrent neural networks for defining Lindblad operators that can keep track of memory effects. Building upon this framework, we also introduce a neural network architecture that is able to reproduce the entire quantum evolution, given an initial state. As an application we study how to train these models for quantum process tomography, showing that recurrent neural networks are accurate over different times and regimes.