论文标题
量子计算机中的噪声指纹：机器学习软件工具
Noise fingerprints in quantum computers: Machine learning software tools
论文作者
论文摘要
在本文中，我们介绍了量子古典机器学习软件的高级功能，其目的是学习影响量子设备的量子噪声源的主要特征（指纹）作为量子计算机。具体而言，该软件体系结构旨在成功对具有相似技术规格的不同量子设备中的噪声指纹进行成功分类（超过精度的99％），或者单个量子指纹在单量子机器中的不同时间依赖性。
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.
