PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 26, 2026Russian Microelectronics0 citations

Kraus Maps and Complex Stiefel Manifolds

View Full Paper
IRI. T. RusskikhSteklov Mathematical InstituteBVB. O. VolkovSteklov Mathematical InstituteAPA. N. PechenNational University of Science and Technology

Key Points

  • The central aim is to investigate the role of complex Stiefel manifolds in representing quantum channels through Kraus maps.
  • Review of geometric properties of quantum channels as orbits of unitary groups
  • Examination of the quotient space geometry of complex Stiefel manifolds
  • Discussion of the implications for Riemannian optimization
  • Showed that representing quantum channels provides insight into their geometric structures
  • Demonstrated the absence of suboptimal extrema in broad classes of quantum control objectives
  • Highlighted the connection between optimization techniques and quantum theory applications

Abstract

Optimization on matrix manifolds, particularly on complex Stiefel manifolds, plays an important role in various tasks of modern quantum theory. The dynamics of open quantum systems are described by quantum channels, also known as Kraus maps. Properties of the geometric structure of the set of quantum channels are essential for various tasks in diverse areas of quantum theory ranging from quantum information theory to quantum computing and quantum control. This review summarizes the results on representing quantum channels as orbits of a unitary group acting on the complex Stiefel manifold and examines the corresponding quotient space geometry. The implication of this framework for Riemannian optimization is demonstrated with a particular emphasis on the absence of suboptimal extrema for broad classes of quantum control objective functionals.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Russkikh et al. (2025) studied this question.

synapsesocial.com/papers/69c4cc02fdc3bde44891769ehttps://doi.org/10.1134/s1063739725601675
Ask AI
Helpful
Bookmark
Share
View Full Paper