Noise fingerprints in quantum computers: Machine learning software tools
University of Florence（佛罗伦萨大学）；European Laboratory for Non-Linear Spectroscopy (LENS)（欧洲非线性光谱实验室（LENS））；University of Lisbon（里斯本大学）；Instituto de Telecomunicações（电信研究所）；CNR-INO（意大利国家研究委员会-国家光学研究所）；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（国际高等研究院（SISSA））
Stefano Martina, Stefano Gherardini, Lorenzo Buffoni, Filippo Caruso
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.
University of Florence（佛罗伦萨大学）；European Laboratory for Non-Linear Spectroscopy (LENS)（欧洲非线性光谱实验室（LENS））；University of Lisbon（里斯本大学）；Instituto de Telecomunicações（电信研究所）；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（国际高等研究学院（SISSA））
Spectral Fingerprints of Gauge Theories on a Quantum Computer
School of Physics and Astronomy, University of Southampton（南安普顿大学物理与天文学院）；Infleqtion UK（英飞克特英国公司）；Department of Physics and Center for Functional Materials, Wake Forest University（维克森林大学物理系和功能材料中心）
RotorMap and Quantum Fingerprints of DNA Sequences via Rotary Position Embeddings
Institute of Mathematics of NAS of Ukraine（乌克兰国家科学院数学研究所）；Kyiv Academic University（基辅学术大学）；Institute of Physics of NAS of Ukraine（乌克兰国家科学院物理研究所）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）
