2020/12/18 by Yuqin Chen, Yu-Qin Chen, Yi-Cong Zheng +7
Computer Science · Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Blind Source Separation Techniques #Characterization (materials science) #Computer science #FOS: Physical sciences #Markov process #Mathematics #Noise (video) #Optics #Physics #Pure mathematics #Quantum Physics (quant-ph) #Scientific Research and Discoveries #Sparse and Compressive Sensing Techniques #Statistical physics #Statistics #Tensor (intrinsic definition) #Transfer (computing) #quant-ph
paper · pdf · doi:10.48550/arxiv.2012.10094
published in arXiv (Cornell University) (Cornell University)
arxiv created 2020/12/18 · openalex publication_date 2020/12/18 · arxiv updated 2020/12/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
With continuing improvements on the quality of fabricated quantum devices, it becomes increasingly crucial to analyze noisy quantum process in greater details such as characterizing the non-Markovianity in a quantitative manner. In this work, we propose an experimental protocol, termed Spectral Transfer Tensor Maps (SpecTTM), to accurately predict the RHP non-Markovian measure of any Pauli channels without state-preparation and measurement (SPAM) errors. In fact, for Pauli channels, SpecTTM even allows the reconstruction of highly-precised noise power spectrum for qubits. At last, we also discuss how SpecTTM can be useful to approximately characterize non-Markovianity of non-Pauli channels via Pauli twirling in an optimal basis.