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Experimental neural network enhanced quantum tomography
npj Quantum Information ( IF 7.6 ) Pub Date : 2020-02-07 , DOI: 10.1038/s41534-020-0248-6
Adriano Macarone Palmieri , Egor Kovlakov , Federico Bianchi , Dmitry Yudin , Stanislav Straupe , Jacob D. Biamonte , Sergei Kulik

Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state-preparation-and-measurement (SPAM) apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to SPAM errors degrading reconstruction performance. Here we develop a framework based on machine learning which generally applies to both the tomography and SPAM mitigation problem. We experimentally implement our method. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to an SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10% and 27%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.



中文翻译:

实验神经网络增强量子层析成像

量子层析成像技术目前普遍用于测试量子信息处理设备的任何实现。用于从测量数据重建状态和过程的各种复杂过程都得到了很好的开发,并且受益于描述状态准备和测量(SPAM)设备的模型的精确知识。但是,物理模型受到固有的限制,因为无法精确地知道实际的测量算子和试验状态。这种情况不可避免地导致SPAM错误,从而降低重建性能。在这里,我们开发了一个基于机器学习的框架,该框架通常适用于断层扫描和SPAM缓解问题。我们通过实验实现了我们的方法。我们训练了一个监督神经网络来过滤实验数据,从而发现了显着的模式,这些模式表征了原始状态和没有SPAM误差的理想实验设备的测量概率。我们将神经网络状态重建协议与通过过程层析成像处理SPAM错误的协议进行了比较,并与具有理想测量结果的SPAM不可知协议进行了比较。平均重建保真度显示分别提高了10%和27%。提出的方法适用于依赖层析成像的大量量子实验。以及与垃圾邮件无关的协议(具有理想的测量值)。平均重建保真度分别提高了10%和27%。提出的方法适用于依赖层析成像的大量量子实验。以及与垃圾邮件无关的协议(具有理想的测量值)。平均重建保真度显示分别提高了10%和27%。提出的方法适用于依赖层析成像的大量量子实验。

更新日期:2020-02-07
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