Machine learning versus semidefinite programming approach to a particular problem of the theory of open quantum systems

DOI10.1134/S199508022107026XzbMath1470.81039OpenAlexW3193052931MaRDI QIDQ2046413

S. V. Denisov, I. I. Yusipov, Mikhail V. Ivanchenko, V. D. Volokitin, I. B. Meyerov, A. V. Liniov

Publication date: 18 August 2021

Published in: Lobachevskii Journal of Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1134/s199508022107026x

zbMATH Keywords

integer programmingopen quantum systemsmachine learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Complexity and performance of numerical algorithms (65Y20) Open systems, reduced dynamics, master equations, decoherence (81S22) Networks and circuits as models of computation; circuit complexity (68Q06)

Uses Software

• Scikit
• PyTorch
• ImageNet
• Adam

Cites Work

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• Calculating Floquet states of large quantum systems: a parallelization strategy and its cluster implementation
• The complexity of relating quantum channels to master equations
• Quantum dynamical semigroups and applications. (Based on Lectures given at the 20th Symposium on Theoretical Chemistry held 1984 in Emmetten, Switzerland)
• On the generators of quantum dynamical semigroups
• Quantum Computation and Quantum Information
• Assessing Non-Markovian Quantum Dynamics
• Completely positive dynamical semigroups of N-level systems

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