2020/09/30 by F. F. Fanchini, Felipe F. Fanchini, Göktuğ Karpat +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Computer science #Degree (music) #Machine learning #Markov process #Mathematics #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum entanglement #Quantum mechanics #Statistics #TRACE (psycholinguistics) #Theoretical computer science #quant-ph
paper · pdf · doi:10.1103/physreva.103.022425
published as Phys. Rev. A 103, 022425 (2021) · 8 pages, 5 figures + Appendix
openalex publication_date 2021/02/24 · arxiv created 2021/03/02 · arxiv updated 2021/03/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
In the last few years, the application of machine learning methods has become increasingly relevant in different fields of physics. One of the most significant subjects in the theory of open quantum systems is the study of the characterization of non-Markovian memory effects that emerge dynamically throughout the time evolution of open systems as they interact with their surrounding environment. Here we consider two well-established quantifiers of the degree of memory effects, namely, the trace distance and the entanglement-based measures of non-Markovianity. We demonstrate that using machine learning techniques, in particular, support vector machine algorithms, it is possible to estimate the degree of non-Markovianity in two paradigmatic open system models with high precision. Our approach can be experimentally feasible to estimate the degree of non-Markovianity, since it requires a single or at most two rounds of state tomography.