arXiv 2103.064572021-03quant-ph

全光神经网络量子态层析成像

All-optical neural network quantum state tomography

Ying Zuo, Chenfeng Cao, Ningping Cao, Xuanying Lai, Bei Zeng, Shengwang Du

摘要：

量子态层析成像（QST）是实验量子信息处理的几乎所有方面的关键组成部分。作为量子环境中“成像”技术的模拟，QST天生就是一个数据科学问题，其中机器学习技术，特别是神经网络，已经得到了广泛的应用。在这项工作中，我们建立了一个集成的全光设置的神经网络QST，基于全光神经网络（AONN）。我们的AONN配备了内置的非线性激活功能，该功能基于电磁感应透明。实验结果验证了该全光系统的有效性和可行性，表明AONN能有效地减小态制备和测量误差，并能准确地预测量子态的相位参数。考虑到未来量子网络非常需要光学设置，我们集成AONN-QST的全光设置可能会为全光量子网络提供最后一块砖。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

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