Randomised benchmarking for characterizing and forecasting correlated processes
Abstract
The development of fault-tolerant quantum processors relies on the ability to control noise. A particularly insidious form of noise is temporally correlated or non-Markovian noise. By combining randomized benchmarking with supervised machine learning algorithms, we develop a method to learn the details of temporally correlated noise. In particular, we can learn the time-independent evolution operator of system plus bath and this leads to (i) the ability to characterize the degree of non-Markovianity of the dynamics and (ii) the ability to predict the dynamics of the system even beyond the times we have used to train our model. We exemplify this by implementing our method on a superconducting quantum processor. Our experimental results show a drastic change between the Markovian and non-Markovian regimes for the learning accuracies.
Cite
@article{arxiv.2312.06062,
title = {Randomised benchmarking for characterizing and forecasting correlated processes},
author = {Xinfang Zhang and Zhihao Wu and Gregory A. L. White and Zhongcheng Xiang and Shun Hu and Zhihui Peng and Yong Liu and Dongning Zheng and Xiang Fu and Anqi Huang and Dario Poletti and Kavan Modi and Junjie Wu and Mingtang Deng and Chu Guo},
journal= {arXiv preprint arXiv:2312.06062},
year = {2025}
}
Comments
4 pages, 3 figures