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November 10, 2026
Physicists Train AI to Optimise Particle Beams in Accelerators
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science and the Budker Institute of Nuclear Physics at the Siberian Branch of the Russian Academy of Sciences have developed RLABC (Reinforcement Learning for Accelerator Beamline Control), a framework that uses reinforcement learning to identify optimal settings for a particle accelerator. In a computer simulation, RLABC optimised the accelerator's parameters to achieve a particle transmission rate of 70%. The study has been published in Computer Physics Communications.
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Researchers at HSE MIEM have shown that widely used simulators of vehicle-to-vehicle communication significantly overestimate its reliability because they assume flat terrain. When terrain features, bridges, and tunnels are taken into account, the outage rate on a mountain serpentine road rises from 21% to 30%, on an overpass from 20% to 33%, and in a tunnel from 0% to 80%. The results have been published in Future Transportation.

 

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Modelling optimal implementation of an arbitrary N-qubit quantum gate within the generalized Bloch vectors formalism

Quantum Information Processing. 2026. Vol. 25. Article 358.
Elena R. Loubenets, Sergey Kuznetsov

The optimal implementation of quantum gates for closed $N$-qubit systems is one of the key challenges for practical realization of many quantum information processing tasks. In the present article, based on the generalized Bloch vectors formalism [J. Phys. A: Math. Theor. 54, 195301 (2021)] for a finite-dimensional quantum system, we develop a new \emph{general} model for the optimal implementation of quantum gates, which is formulated in terms of the Bloch vectors for the unitary evolution operator and the system Hamiltonians, drift and control, and has the \textcolor{red}{unified mathematical form,  which is standard for the mathematical control theory and applicable} for the implementation of an arbitrary N-qubit gate within any closed N-qubit system, satisfying the controllability conditions. Within the developed optimal model, the cost functional has both the terminal part and also, the integral part with the special scaling, and this allows us to specify the  quantum optimal control synthesis via solving the two-point boundary value problem (BVP) for the system of ordinary differential equations (ODEs), which can be explored numerically by any of the known BVP solvers for ODEs. The numerical experiments,  conducted for the implementation within the developed optimal model of a variety of N=1,2,3 qubit gates, demonstrate the high accuracy of the model-based results.

Research target: Mathematics Physics
Language: English
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Keywords: Optimal quantum controlGeneralized Bloch vectors formlalismN-qubit quantum gates implementationOptimal quantum controlGeneralized Bloch vectors formalismN-qubit quantum gates implementation
Publication based on the results of:
Supercomputer modelling and AI methods in topical problems of physics and complex systems (2025)
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