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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
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‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
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HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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Simulation of open quantum systems by automated compression of arbitrary environments

Nature Physics. 2022. Vol. 18. No. 6. P. 662–668.
Cygorek M., Cosacchi M., Vagov A., Axt V. M., Lovett B., Keeling J., Gaugner E.

Studies of the dynamics of open quantum systems are limited by the large Hilbert space of typical environments, which is too large to be treated exactly. In some cases, approximate descriptions of the system are possible, for example, when the environment has a short memory time or only interacts weakly with the system. Accurate numerical methods exist, but these are typically restricted to baths with Gaussian correlations, such as non-interacting bosons. Here we present a method for simulating open quantum systems with arbitrary environments that consist of a set of independent degrees of freedom. Our approach automatically reduces the large number of environmental degrees of freedom to those which are most relevant. Specifically, we show how the process tensor describing the effect of the environment can be iteratively constructed and compressed using matrix product state techniques. We demonstrate the power of this method by applying it to a range of open quantum systems, including bosonic, fermionic and spin environments. The versatility and efficiency of our automated compression of environments method provides a practical general-purpose tool for open quantum systems.

Research target: Physics Mathematics Computer Science Nanotechnologies
Language: English
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Keywords: сложные системыcomplex systemsopen quantum systemsоткрытые квантовые системыFeynman path integralинтегралы по траекториям Фейнманаtensor networksтензорные сети
Publication based on the results of:
Investigations of quantum effects in low-dimensional materials  (2022)
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