2023/03/15 by Emilio Onorati, Onorati, Emilio, Tamara Kohler +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #A priori and a posteriori #Algorithm #Artificial intelligence #Channel (broadcasting) #Computer science #FOS: Physical sciences #Machine learning #Markov chain #Markov process #Mathematics #Noise (video) #Operator (biology) #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum Physics (quant-ph) #Set (abstract data type) #Statistical physics #Theoretical computer science
paper · pdf · doi:10.48550/arxiv.2303.08936
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/03/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
Understanding how to characterise and mitigate errors is a key challenge in developing reliable quantum architecture for near-term applications. Recent work (arXiv:2103.17243) provides an efficient set of algorithms for analysing unknown noise processes requiring only tomographic snapshots of the quantum operator under consideration, without the need of any a-priori information on the noise model, nor necessitating a particular experimental setup. The only assumption made is that the observed channel can be approximated by a time-independent Markovian map, which is a typically reasonable framework when considering short time scales. In this note we lift the time-independent assumption, presenting an extension of the scheme now able to analyse noisy dynamics with time-dependent generators from a sequence of snapshots. We hence provide a diagnostic tool for a wider spectrum of instances while inheriting all the favourable features from the previous protocol. On the theoretical side, the problem of characterising time-dependent Markovian channels has been an open problem for many decades. This work gives an approach to tackle this characterisation problem rigorously.