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arXiv 2103.01535quant-ph
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利用机器学习对量子断层扫描的完整性和保真度进行基准测试

Benchmarking quantum tomography completeness and fidelity with machine learning

Yong Siah Teo, Seongwook Shin, Hyunseok Jeong, Yosep Kim, Yoon-Ho Kim, Gleb I. Struchalin, Egor V. Kovlakov, Stanislav S. Straupe, Sergei P. Kulik, Gerd Leuchs,… 展开作者

Yong Siah Teo, Seongwook Shin, Hyunseok Jeong, Yosep Kim, Yoon-Ho Kim, Gleb I. Struchalin, Egor V. Kovlakov, Stanislav S. Straupe, Sergei P. Kulik, Gerd Leuchs, Luis L. Sanchez-Soto

摘要:

我们训练卷积神经网络来预测一组测量是否在信息上完整,以在没有先验信息的情况下唯一地重建任何给定的量子态。此外,我们执行保真度基准测试的基础上,这个测量集没有明确进行状态断层扫描。网络经过训练,可以识别保真度和信息完整性的可靠度量。通过逐渐积累测量和数据,这些经过训练的卷积网络可以通过加速运行时计算并大大减少实验中的系统漂移来有效地建立压缩量子态表征方案。我们确认了这种机器学习方法的潜力,提出了大尺寸的空间模式和多光子系统的实验结果。当网络使用来自真实实验数据的额外自举训练集进行训练时,这些预测进一步得到改善。使用厄米-高斯源的实际光束轮廓位移误差模型,我们进一步在数值上证明,在状态保真度显着恶化之前,经过训练的网络的认证时间的数量级减少大大增加了使用这些源的大规模量子处理器的计算产量。

英文摘要:

We train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly.

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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