Kacskovics Balázs[1,2] and Barta Dániel[1] (2023.2.13 - 8.15)

[1] Wigner Research Centre for Physics
[2] PTE Doctoral School of Physics

Abstract: We are modeling equilibrium configurations of rapidly rotating compact stars for various equation of states (EOS) including nuclear, hybrid and quark matter models. Apart from angular momentum we also will include into our investigation the temperature to go beyond the scope of cold-matter EOSs. In order to find a common ground with gravitational-wave observations we will compute the tidal Love-numbers of such stars. The physical parameters are determined by simulations written in the LORENE code library, which applies multi-domain spectral methods for numerically solving the 3+1 decomposition of Einstein equations.

Ákos Gellért[1,2] , Oz Kilim[1] , Anikó Mentes[1] and István Csabai[1] (2023.02.15 - 2023.12.15)

[1] ELTE Department of Physics of Complex Systems
[2] ELKH Veterinary Medical Research Institute

Abstract: The first recorded pandemic of the flu occurred in 1580 and since then, flu pandemics have occurred several times throughout history, with the most severe being the Spanish flu in 1918-1919 which killed millions of people worldwide. In the 20th century, significant progress was made in the understanding of the virus and the development of vaccines, which have greatly reduced the impact of flu pandemics. Despite this progress, the flu continues to be a major public health issue, with millions of cases reported each year and an annual death toll in the tens of thousands.

Hemagglutinin, a surface membrane protein of the Influenza virus plays an important role in the infection process of the virus, as it allows the virus to attach to and penetrate host cells. The flu vaccine is formulated each year based on which strains of the virus are predicted to be most prevalent, and it is designed to stimulate the body's immune response to the hemagglutinin protein on those strains. Many antigenic maps have been constructed this far, which reveal the relationships between different strains of a virus, specifically with regards to the way their antigens [1] (e.g., hemagglutinin) are recognized by the immune system. Experimental Influenza HA deep mutational data [2] are also available for the research community to explore the virus functions.

In this project, we aim to in silico combine antigenic maps and deep mutational scanning data to obtain a more comprehensive understanding of the evolution and functional properties of Influenza virus. For example, combining antigenic map data with deep mutational scanning data can provide information about how different mutations affect the ability of a virus to evade the immune response, as well as which regions of the virus are critical for this evasion. This information can be used to inform the design of vaccines and antiviral drugs that target specific regions of the virus that are critical for its function and evolution. We will use AlphaFold2 [3] and ESMFold2 [4] the fastest AI based and most reliable protein structure prediction applications in the world to generate single and/or multiple mutant structures of various Influenza HA protein.

[1] Antigenic map.
[2] Flu HA DMS..
[3] J. Jumper et al., “Highly accurate protein structure prediction with AlphaFold,” Nat. 2021 5967873, vol. 596, no. 7873, pp. 583–589, Jul. 2021, doi: 10.1038/s41586-021-03819-2.
[4] ESMFold.

Bedőházi Zsolt[1,2] and Biricz András[1] (2023.02.13 - 05.15)

[1] ELTE Department of Complex Systems in Physics
[2] ELTE Doctoral School of Informatics

Abstract: The application of deep learning in gigapixel whole slide image analysis has shown promising results in terms of accuracy and efficiency compared to traditional image analysis techniques. Transformer based models as the current state-of-the-art algorithms are designed to identify and classify various structures and patterns within the tissue, providing insights into the underlying pathology and helping in the diagnosis and treatment of diseases. We are currently working on multiple projects in this field including breast cancer stage prediction [1] and colorectal cancer detection [2].

Ágoston Kaposi (ELTE), Zoltán Kolarovszki (ELTE), Tamás Kozsik (ELTE), Zoltán Zimborás (Wigner FK) and Péter Rakyta (ELTE)
(2022.05.01 - 2022.12.31)

Abstract: Evaluating the Torontonian function is a central computational challenge in the simulation of Gaussian Boson Sampling (GBS) with threshold detection. In this work, we propose a recursive algorithm providing a polynomial speedup in the exact calculation of the Torontonian compared to state-of-the-art algorithms. According to our numerical analysis the complexity of the algorithm is proportional to N1.06912N/2 with N being the size of the problem. We also show that the recursive algorithm can be scaled up to HPC use cases making feasible the simulation of threshold GBS up to 35−40 photon clicks without the needs of large-scale computational capacities.

Publications: Ágoston Kaposi, Zoltán Kolarovszki, Tamás Kozsik, Zoltán Zimborás, Péter Rakyta: Polynomial speedup in Torontonian calculation by a scalable recursive algorithm ArXiv:2109.04528

Emese Forgács-Dajka*, István Balla** (2021.05.01-2021.12.31)

* Eötvös University, Dept. of Astronomy
** Solar Physics and Space Plasma Research Centre (SP2RC), Department of Applied Mathematics, The University of Sheffield

Abstract: We investigate the nature and properties of shock waves propagating in an oblique direction to the ambient magnetic field in a partially ionised plasma modelling the plasma of solar prominences. In particular, we aim to analyse the observational signature of these shocks and investigate how our results can explain the recent observations of propagating bright blobs in solar prominences by Lin et al. (2012).

The equations of compressional single-fluid magnetohydrodynamic (MHD) equations are reduced with the help of a multiple scaling method to a well-known Burgers equation whose coefficients depend on the propagation angle of shock waves, plasma-β and the ionisation degree of the plasma. Our model is well-adapted for the separate discussion of shock waves arising from the nonlinear steepening of slow or fast magnetoacoustic waves. Using the standard jump conditions across the shock front (assuming a weak dissipation) we determine the jump in thermodynamic quantities that will be useful for comparison with observations.

Using the Cole-Hopf transform we solve the governing equation as an initial value problem of a diffusion-like equation and investigate the time necessary for a Gaussian initial wave profile to evolve into a shock, whose thickness is of the order of a few ion mean free path.

Péter Rakyta (ELTE, Wigner RCP), Gregory Morse (ELTE), Jakab Nádori (ELTE), Oskar Mencer (Maxeler Technologies), Zoltán Zimborás (Wigner RCP) (2022.05.01 - 2022.12.31)

Publication: Highly optimized quantum circuits synthesized via data-flow engines

Abstract: The formulation of quantum programs in terms of the fewest number of gate operations is crucial to retrieve meaningful results from the noisy quantum processors accessible these days. In this work we demonstrate a use case for Field Programmable Gate Array (FPGA) based data-flow engines (DFEs) to scale up optimization based quantum compilers to synthesize circuits up to 9-qubit programs. The developed DFE quantum computer simulator was designed to simulate arbitrary quantum circuit consisting of single qubit rotations and controlled two-qubit gates on FPGA chips. In our benchmark with the QISKIT package, the depth of the circuits produced by the SQUANDER package (with the DFE accelerator support) were less by 97% on average, while the fidelity of the circuits was still close to unity by an error of ∼ 10−4. I

István Papp, Larissa Bravina, Mária Csete, Igor N. Mishustin, Dénes Molnár, Anton Motornenko, Leonid M. Satarov, Horst Stöcker, Daniel D. Strottman, András Szenes, Dávid Vass, Tamás S. Biró, László P. Csernai, Norbert Kroó (2022.07.01 - 2022.12.31)

Abstract: Our dependence on fossil fuels grew more and more in the last century and today we are urged to find alternative energy sources. Laser driven fusion is a promising option for clean and safe energy production. The most successful configuration up to now uses indirect drive, the thermal radiation coming from a cylindrical Hohlraum. After the target is compressed by the incoming light, it develops Rayleigh-Taylor instabilities. An ongoing activity at Wigner Research Centre’s Nanoplasmonic Laser Fusion National Laboratory (NAPLIFE) collaboration is aiming for improving the chances of fusion by high-power short laser pulses and target fabrication, combining recent discoveries in heavy-ion collisions and optics [1]. Our aim is studying in simulations the surface plasmonic effect of resonant gold nano-antennas in different monomer mediums. The monomer serves only experimental purposes, proving the effectiveness of the nanorods. The plasmonic effect is vital for the project, since it will be used to manipulate the target’s absorption properties. The different layers of monomers with different gold nanoparticle densities will be studied, taking into account the lifetime of plasmons using a kinetic plasma model for conducting electrons [2]. The results will be essencial for future experiments in ELI-ALPS Szeged laser facility.

[1] L.P Csernai, N. Kroo and I. Papp, Radiation dominated implosion with nanoplasmonics, Laser and Particle Beams, Volume 36, Issue 2, June 2018 , pp. 171-178

[2] I. Papp, L. Bravina, M. Csete, et al., Kinetic model evaluation of the resilience of plasmonic nanoantennas for laser-induced fusion, PRX Energy, Vol. 1, Iss. 2 (2022)