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arXiv 2106.04058quant-ph
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压缩态退化信息的机器学习提取

Extract the Degradation Information in Squeezed States with Machine Learning

Hsien-Yi Hsieh, Yi-Ru Chen, Hsun-Chung Wu, Huali Chen, Jingyu Ning, Yao-Chin Huang, Chien-Ming Wu, Ray-Kuang Lee

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15 pages, 6 figures
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摘要：

为了充分利用量子噪声压缩和不可避免的退相干，需要完全理解压缩光纯度的下降。通过使用卷积神经网络实现机器学习架构，我们通过平衡零差检测器生成的实验测量数据说明了连续变量的快速，鲁棒和精确的量子态断层扫描。相对于极大似然估计方法的耗时和过拟合问题，一个受过良好训练的压缩真空和压缩热态机器可以在不到1秒的时间内完成密度矩阵的重构任务。此外，即使当抗挤压水平高于$20$ ~dB时，所得到的保真度也保持与$0.99$一样高。与环境和周围真空耦合的相位噪声和损耗机制相比，实验上，通过机器学习揭示了低噪声和高噪声场景下的退化信息，即，反压缩能级分别为$12$ ~dB和$18$ ~dB。我们的神经网络增强型量子态层析成像提供了度量，可以通过单次测量对量子态中观察到的每个特征进行物理描述，并为实时探索大规模量子系统铺平了道路。

英文摘要：

In order to leverage the full power of quantum noise squeezing with unavoidable decoherence, a complete understanding of the degradation in the purity of squeezed light is demanded. By implementing machine learning architecture with a convolutional neural network, we illustrate a fast, robust, and precise quantum state tomography for continuous variables, through the experimentally measured data generated from the balanced homodyne detectors. Compared with the maximum likelihood estimation method, which suffers from time-consuming and over-fitting problems, a well-trained machine fed with squeezed vacuum and squeezed thermal states can complete the task of reconstruction of the density matrix in less than one second. Moreover, the resulting fidelity remains as high as $0.99$ even when the anti-squeezing level is higher than $20$~dB. Compared with the phase noise and loss mechanisms coupled from the environment and surrounding vacuum, experimentally, the degradation information is unveiled with machine learning for low and high noisy scenarios, i.e., with the anti-squeezing levels at $12$~dB and $18$~dB, respectively. Our neural network enhanced quantum state tomography provides the metrics to give physical descriptions of every feature observed in the quantum state with a single-shot measurement and paves a way of exploring large-scale quantum systems in real-time.

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。

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