论文标题
通过专门的神经网络改进的层析成像估计
Improved Tomographic Estimates by Specialised Neural Networks
论文作者
论文摘要
量子对象的表征,它们是状态,过程或测量值,并以先前的知识进行补充是一种有价值的方法,尤其是因为它导致了现实生活中成分的常规程序。为此,机器学习算法已证明在存在噪声的情况下成功地操作,尤其是用于估计特定的物理参数。在这里,我们表明神经网络(NN)可以通过包括卷积阶段来改善参数的层析成像估计值。我们将技术应用于量子过程断层扫描,以表征多个量子通道。我们证明,仅通过模拟数据训练网络可以实现稳定且可靠的操作。获得的结果表明,这种方法作为一种有效工具的生存能力,基于一个全新的范式,用于使用量子系统生产的经典数据运行的NNS。
Characterization of quantum objects, being them states, processes, or measurements, complemented by previous knowledge about them is a valuable approach, especially as it leads to routine procedures for real-life components. To this end, Machine Learning algorithms have demonstrated to successfully operate in presence of noise, especially for estimating specific physical parameters. Here we show that a neural network (NN) can improve the tomographic estimate of parameters by including a convolutional stage. We applied our technique to quantum process tomography for the characterization of several quantum channels. We demonstrate that a stable and reliable operation is achievable by training the network only with simulated data. The obtained results show the viability of this approach as an effective tool based on a completely new paradigm for the employment of NNs operating on classical data produced by quantum systems.