Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory

TL;DR

This paper employs recurrent neural networks to optimize dynamical decoupling sequences in quantum memory, demonstrating improved error suppression over traditional methods through machine learning-driven sequence design.

Contribution

It introduces a machine learning approach using RNNs to optimize dynamical decoupling sequences, adaptable to specific hardware with minimal prior knowledge.

Findings

RNN-based models outperform traditional DD sequences in simulations

The method requires minimal prior knowledge and starts from random sequences

The approach is easily implementable in experimental settings

Abstract

We utilize machine learning models which are based on recurrent neural networks to optimize dynamical decoupling (DD) sequences. DD is a relatively simple technique for suppressing the errors in quantum memory for certain noise models. In numerical simulations, we show that with minimum use of prior knowledge and starting from random sequences, the models are able to improve over time and eventually output DD-sequences with performance better than that of the well known DD-families. Furthermore, our algorithm is easy to implement in experiments to find solutions tailored to the specific hardware, as it treats the figure of merit as a black box.