activity
20182025
most citedMulti-scale structural complexity of natural patterns

69 citations · 242 across the 14 of their papers we have counts for

collaborators

14 papers

quant-ph2025

Quantum-optical reset with classical memory

Evgeniy O. Kiktenko, Oleg M. Sotnikov, Ilia A. Iakovlev +5

Dynamic quantum circuits generate states that depend on the measurement results obtained during circuit execution. To date such a quantum computing model has mainly been implemente…

quant-ph2024★ 1 cited

Emergence of global receptive fields capturing multipartite quantum correlations

Oleg M. Sotnikov, Ilia A. Iakovlev, Evgeniy O. Kiktenko +3

In quantum physics, even simple data with a well-defined structure at the wave function level can be characterized by extremely complex correlations between its constituent element…

quant-ph2024★ 3 cited

Achieving the volume-law entropy regime with random-sign Dicke states

Oleg M. Sotnikov, Ilia A. Iakovlev, Evgeniy O. Kiktenko +2

Manipulating entanglement, which reflects non-local correlations in a quantum system and defines the complexity of describing its wave function, represents the extremely tough chal…

quant-ph2023★ 6 cited

Benchmarking a boson sampler with Hamming nets

Ilia A. Iakovlev, Oleg M. Sotnikov, Ivan V. Dyakonov +4

Analyzing the properties of complex quantum systems is crucial for further development of quantum devices, yet this task is typically challenging and demanding with respect to requ…

cond-mat.str-el2023★ 17 cited

Estimating Patterns of Classical and Quantum Skyrmion States

Vladimir V. Mazurenko, Ilia A. Iakovlev, Oleg M. Sotnikov +1

In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protect…

cond-mat.mtrl-sci2023★ 8 cited

An effective spin model on the honeycomb lattice for the description of magnetic properties in two-dimensional FeGeTe

Georgy V. Pushkarev, Danis I. Badrtdinov, Ilia A. Iakovlev +2

FeGeTe attracts significant attention due to technological perspectives of realizing room temperature ferromagnetism in two-dimensional materials. Here we show that due to…

quant-ph2023★ 4 cited

Quantifying spatio-temporal patterns in classical and quantum systems out of equilibrium

E. A. Maletskii, I. A. Iakovlev, V. V. Mazurenko

A rich variety of non-equilibrium dynamical phenomena and processes unambiguously calls for the development of general numerical techniques to probe and estimate a complex interpla…

quant-ph2021★ 19 cited

Certification of quantum states with hidden structure of their bitstrings

O. M. Sotnikov, I. A. Iakovlev, A. A. Iliasov +3

The rapid development of quantum computing technologies already made it possible to manipulate a collective state of several dozen of qubits. This success poses a strong demand on…

cond-mat.str-el2020★ 14 cited

Field evolution of the spin-liquid candidate YbMgGaO

Sebastian Bachus, Ilia A. Iakovlev, Yuesheng Li +7

We report magnetization, heat capacity, thermal expansion, and magnetostriction measurements down to millikelvin temperatures on the triangular antiferromagnet YbMgGaO. Our dat…

nlin.PS2020★ 69 cited

Multi-scale structural complexity of natural patterns

Andrey A. Bagrov, Ilia A. Iakovlev, Askar A. Iliasov +2

Complexity of patterns is a 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…

cond-mat.str-el2019★ 8 cited

Recurrent network classifier for ultrafast skyrmion dynamics

A. Y. Deviatov, I. A. Iakovlev, V. V. Mazurenko

By using the supervised learning we train a recurrent neural network to recognize and classify ultrafast magnetization processes realized in two-dimensional nanosystems with Dzyalo…

cond-mat.str-el2018★ 15 cited

Profile approach for recognition of three-dimensional magnetic structures

I. A. Iakovlev, O. M. Sotnikov, V. V. Mazurenko

We propose an approach for low-dimensional visualisation and classification of complex topological magnetic structures formed in magnetic materials. Within the approach one convert…

cond-mat.str-el2018★ 55 cited

Supervised learning magnetic skyrmion phases

I. A. Iakovlev, O. M. Sotnikov, V. V. Mazurenko

We propose and apply simple machine learning approaches for recognition and classification of complex non-collinear magnetic structures in two-dimensional materials. The first appr…

cond-mat.str-el2018★ 23 cited

Bimeron nanoconfined design

I. A. Iakovlev, O. M. Sotnikov, V. V. Mazurenko

We report on the stabilization of the topological bimeron excitations in confined geometries. The Monte Carlo simulations for a ferromagnet with a strong Dzyaloshinskii-Moriya inte…