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arXiv 2303.00909quant-ph
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用于量子比特噪声谱学的随机脉冲序列

Random Pulse Sequences for Qubit Noise Spectroscopy

Kaixin Huang, Demitry Farfurnik, Alireza Seif, Mohammad Hafezi, Yi-Kai Liu

AI总结:

本文提出一种基于随机脉冲序列的量子比特噪声谱学方法,利用相位恢复算法设计序列并直接测量噪声谱线性泛函,结合压缩感知重建稀疏谱,在自组装量子点上实现比传统动力学解耦快一个数量级的谱提取。

摘要:

量子比特噪声谱学是实验研究开放量子系统的重要工具。然而,传统的噪声谱学技术耗时较长,因为它们需要在许多不同频率下测量噪声谱密度。在此,我们描述了一种替代性的噪声谱学方法,该方法所需资源更少,并依赖于对噪声谱密度的任意线性泛函的直接测量。该方法使用具有精心控制的相关性的随机脉冲序列,这些序列通过相位恢复算法进行选择。这些测量使我们能够通过压缩感知重建稀疏的噪声谱。我们在一个现实物理系统——自组装量子点上对随机脉冲序列性能的模拟表明,与传统动力学解耦方法相比,提取噪声谱的速度提升了一个数量级。

英文摘要:

Qubit noise spectroscopy is an important tool for the experimental investigation of open quantum systems. However, conventional techniques for noise spectroscopy are time-consuming, because they require measurements of the noise spectral density at many different frequencies. Here we describe an alternative approach to noise spectroscopy, which requires fewer resources, and relies on direct measurement of arbitrary linear functionals of the noise spectral density. This method uses random pulse sequences with carefully-controlled correlations, which are chosen using algorithms for phase retrieval. These measurements allow us to reconstruct sparse noise spectra via compressed sensing. Our simulations of the performance of the random pulse sequences on a realistic physical system, self-assembled quantum dots, reveal a speedup of an order of magnitude in extracting the noise spectrum, compared to conventional dynamical decoupling approaches.

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