基于深度学习的量子比特噪声谱解析  

Analysis of Qubit Noise Spectra Based on Deep Learning

作　　者：周雪莹 张文超[1] 胡志毅 周飞飞 陈冰 徐南阳 ZHOU Xue-ying;ZHANG Wen-chao;HU Zhi-yi;ZHOU Fei-fei;CHEN Bing;XU Nan-yang(School of Physics,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学物理学院,安徽合肥230009

出　　处：《量子光学学报》2022年第3期200-207,共8页Journal of Quantum Optics

基　　金：国家自然科学基金面上项目(12174081)。

摘　　要：作为量子技术中的信息载体,量子比特被广泛应用于量子计算、量子模拟以及量子精密测量等研究领域。然而由于环境中如噪声等因素的存在,量子比特的信息表征会受到一定限制。如何精确地解析出环境中作用于量子比特的噪声谱信息,成了量子比特动力学解耦工作中亟待解决的问题。基于近似解析的传统方法无法精确地从量子比特的时域测量信息中解析出噪声谱。因此,在本文中,我们提出了一种基于深度学习的量子比特噪声谱解析方法。该方法通过不断学习,能够获取量子比特的退相干曲线与其噪声谱之间的潜在映射关系,为后续数值模拟和实验测量中的量子比特噪声谱解析提供了新的技术思路。相比于传统方法,该方法的精确度更高,并且可以推广到其他类型噪声谱的解析工作中。As a significant carrier of quantum information,the studies of qubits are providing more opportunities in emerging research fields such as quantum computation,quantum simulation and quantum metrology.However,due to the existence of a noise environment,such as magnetic signal fluctuation,the resulted decoherence of qubits limits its capabilities in the abovementioned fields.Acknowledging the noise information can help us to break the limitation and improve the applications of qubits,based on which crucial optimization of the customized dynamical decoupling protocols and suppression of the noise could be well realized.Thus,an accurate and efficient approach for analyzing the spectral information of the noise environment is required.Because of the difficulty of function inverse solution,conventional analytical methods based on approximation techniques can not accurately resolve the noise spectra from the interrogation-time-domain measurements of qubits.Recently,significant advance has been made in deep learning,which has been widely used for quantum information processing.In this paper,we propose a deep-learning-based method for noise spectral analysis of qubits.This method only needs to input the decoherence curves into the deep-learning model to predict the existed environmental noise spectra.Through a series of iterative studies,this method can extract the potential mapping relation between the decoherence curves of qubits and corresponding noise spectra.We numerically simulate the deep-learning-based analysis process and demonstrate the good performance of our method.Moreover,benefited from the deep-learning-based algorithm,compared with the conventional method,the accuracy and efficiency of our method are much better.Our method also provides a new technique for other noise spectral analysis tasks and could be easily applied to a wide range of quantum systems.

关 键 词：量子比特 噪声谱 动力学解耦 卷积神经网络 退相干曲线 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O413[自动化与计算机技术—控制科学与工程]