All-optical neural network quantum state tomography
Abstract
Quantum state tomography (QST) is a crucial ingredient for almost all aspects of experimental quantum information processing. As an analog of the "imaging" technique in the quantum settings, QST is born to be a data science problem, where machine learning techniques, noticeably neural networks, have been applied extensively. In this work, we build an integrated all-optical setup for neural network QST, based on an all-optical neural network (AONN). Our AONN is equipped with built-in nonlinear activation function, which is based on electromagnetically induced transparency. Experiment results demonstrate the validity and efficiency of the all-optical setup, indicating that AONN can mitigate the state-preparation-and-measurement error and predict the phase parameter in the quantum state accurately. Given that optical setups are highly desired for future quantum networks, our all-optical setup of integrated AONN-QST may shed light on replenishing the all-optical quantum network with the last brick.
Cite
@article{arxiv.2103.06457,
title = {All-optical neural network quantum state tomography},
author = {Ying Zuo and Chenfeng Cao and Ningping Cao and Xuanying Lai and Bei Zeng and Shengwang Du},
journal= {arXiv preprint arXiv:2103.06457},
year = {2021}
}