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Träfflista för sökning "WFRF:(Iakovlev I. A.) srt2:(2020-2022)"

Sökning: WFRF:(Iakovlev I. A.) > (2020-2022)

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1.
  • Sotnikov, O. M., et al. (författare)
  • Certification of quantum states with hidden structure of their bitstrings
  • 2022
  • Ingår i: NPJ QUANTUM INFORMATION. - : Springer Nature. - 2056-6387. ; 8:1
  • Tidskriftsartikel (refereegranskat)abstract
    • The rapid development of quantum computing technologies already made it possible to manipulate a collective state of several dozens of qubits, which poses a strong demand on efficient methods for characterization and verification of large-scale quantum states. Here, we propose a numerically cheap procedure to distinguish quantum states which is based on a limited number of projective measurements in at least two different bases and computing inter-scale dissimilarities of the resulting bit-string patterns via coarse-graining. The information one obtains through this procedure can be viewed as a 'hash function' of quantum state-a simple set of numbers which is specific for a concrete wave function and can be used for certification. We show that it is enough to characterize quantum states with different structure of entanglement, including the chaotic quantum states. Our approach can also be employed to detect phase transitions in quantum magnetic systems.
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2.
  • Bagrov, Andrey A., et al. (författare)
  • Multiscale structural complexity of natural patterns
  • 2020
  • Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : NATL ACAD SCIENCES. - 0027-8424 .- 1091-6490. ; 117:48, s. 30241-30251
  • Tidskriftsartikel (refereegranskat)abstract
    • Complexity of patterns is key information for human brain to differ objects of about the same size and shape. Like other innate human senses, the complexity perception cannot be easily quantified. We propose a transparent and universal machine method for estimating structural (effective) complexity of two-dimensional and three-dimensional patterns that can be straightforwardly generalized onto other classes of objects. It is based on multistep renormalization of the pattern of interest and computing the overlap between neighboring renormalized layers. This way, we can define a single number characterizing the structural complexity of an object. We apply this definition to quantify complexity of various magnetic patterns and demonstrate that not only does it reflect the intuitive feeling of what is "complex" and what is "simple" but also, can be used to accurately detect different phase transitions and gain information about dynamics of nonequilibrium systems. When employed for that, the proposed scheme is much simpler and numerically cheaper than the standard methods based on computing correlation functions or using machine learning techniques.
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