##### Örs Legeza (2023.11.01 - 2024.04.30)

Wigner Research Centre for Physics

**Abstract:** Numerical simulation of quantum systems in which correlations between electrons are strong, i.e., they cannot be described by perturbation theory is in the focus of modern physics and chemistry. This, however, poses major challenge as the computational complexity usually scales exponentially with system size. Therefore, those algorithms in which such scaling can be reduced to polynomial form is subject of intense research.

The density matrix renormalization group (DMRG) method fulfills such criteria. In addition, the related matrix and tensor algebra can be organized into millions of independent subtaks, that makes the method ideal for massive parallelization. Using our code, during the first phase of the project (2021-2022) we have already performed large scale simulations on various quantum systems which lead to two publications accessible on arXiv:

[1] Massively Parallel Tensor Network State Algorithms on Hybrid CPU-GPU Based Architectures, Andor Menczer, Örs Legeza, arXiv:2305.05581 (2023)

[2] Boosting the effective performance of massively parallel tensor network state algorithms on hybrid CPU-GPU based architectures via non-Abelian symmetries, Andor Menczer, Örs Legeza, arXiv:2309.16724 (2023)

The GPU Laboratory is explicitly cited in the acknowledgement in Ref.[1] as part of the results were generated via project phase-1. In the second phase of the project we aim to further test our simulations using A100 GPU based infrastructure. Depending on the results we intend to update or extend results reported in Ref.[2].

##### Ádám Kadlecsik (2023.11.01 - 2024.03.31)

Eötvös Loránd University

**Abstract:** The observed small, thus usually solid exoplanets in general orbit their central star closely - making them easier to detect with terrestrial and space instruments. This means that they must be tidally locked, meaning their orbit around their central star ("year") and their rotation around their axis ("day") have the same period. Because of the tidally locked orbit the exoplanet shows its same side to the star, thus the planet has a permanent day and night hemisphere. Ergo the flow can be modeled with a rotating layout, where the lateral boundary rotating with the water body simulating the atmosphere has an azimuthal dipole-like heat flux boundary condition. This can be investigated using experimental and simulational methods as well.

##### Anna Horváth [1,2], Gergely Gábor Barnaföldi [1], Emese Forgács-Dajka [2] (2023.09.01-2023.12.31 )

[1] Wigner Research Centre for Physics

[2] Eötvös Loránd University

**Abstract:** We are investigating compact stars within a static, spherically symmetric Kaluza-Klein-like theory that encompasses extra compactified spatial dimensions. We produced an equation of state that can be used to model neutron stars together with the Tolman-Oppenheimer-Volkoff equation. Simulating the structure of these objects (calculating their main observables, the mass and the radius) and carrying out a thorough analysis requires us to use computational-heavy programming. Stars with various boundary conditions (such as the central energy density) and theoretical parameters (like the size of the extra dimension) are considered. For this type of calculations it is essentially useful to utilise parallelism, which is best executed on multi-core processors. This project tests theories beyond the standard model of particle physics, with an emphasis on the possibility of giving constraints on the size of one extra compactified spatial dimension.

##### Mira Gergácz, Ákos Keresztúri (2023.09.01-12.31.)

**Abstract:** The aim of this study is to survey the surface ice condensation periods of Martian ice with automatized methods using a Convolutional Neural Network (CNN) applied to High Resolution Imaging Science Experiment (HiRISE) imagery from the Mars Reconnaissance Orbiter (MRO) mission. Using a CNN trained to distinguish small (diameter ranging from \(1.5-300\) meters) ice patches from other surface forms turned out to be a feasible automatic method for this purpose. The surveyed region is the latitude band between \(-40°\) and \(-60°\) during the solar longitude range of \(0-90°\), before the southern polar cap spreads in the given latitude band.

##### Győző Kovács [1], Péter Kovács [1], György Wolf [1], Pok Man Lo [2] (2023.01.01 - 11.30)

[1] Wigner Research Centre for Physics

[2] University of Wroclaw, Wroclaw

**Grant:** NKFIH FK 131982

**Abstract:** The effect of finite size/volume in effective field theory models is usually accounted for by some form of constrain in the momentum space, for example by low momentum cut-off or discretization. The latter results in a summation for momentum modes depending on the boundary condition used, rather than integrals. We study the size dependence of the baryon fluctuations in and around the critical endpoint in an improved quark-meson model, with particular attention to how they are affected by different modifications of the momentum space and the inclusion of the vacuum contribution. To determine the cumulant ratios describing the fluctuations, the higher order derivatives of the pressure are calculated. For this purpose, the finite difference method can be used, but this requires solving the field equations at several points for the derivative at only one point. This demands considerable computational power if the phase diagram and the size dependence are to be computed densely enough. On the other hand these calculations can be easily parallelised.

János Takátsy [1] Péter Kovács [1], György Wolf [1], Juergen Schaffner-Bielich [2] (2023.01.01 - 11.30)

[1] Wigner Research Centre for Physics

[2] University of Frankfurt

**Grant:** NKFIH FK 131982

**Abstract:** The investigation of the phase diagram of Quantum Chromodynamics (QCD) at high densities is currently only possible through effective theories. Neutron stars are among the densest objects in our universe, possibly containing chirally symmetric matter as well. We calculate neutron star properties from our model with different parameterizations and confront the results with recent astrophysical observations. These astrophysical observations include mass measurements of neutron stars, NICER measurements and tidal deformability measurements of GW170817. We utilize a Bayesian framework to determine the most probable regions of the parameter space of our model, which requires high computing capacities in order to obtain the necessary statistics.

##### Győző Kovács, Péter Kovács, György Wolf (2023.01.01 - 11.30)

Wigner Research Centre for Physics

**Grant:** NKFIH FK 131982

**Abstract:** The extended linear sigma model (eLSM) is an advanced quarkmeson model that can be used to study meson phenomenology at zero temperature and the QCD phase diagram at finite temperature and/or baryon chemical potential. In order to make predictions, parameters of the model should be determined by fitting calculated physical quantities (like masses and decay widths) to their experimental values. The best fit and global minimum can be found by choosing some random starting points in the k-dimensional parameter space (in our case \(k > 14\)) and run a \(\chi^2\) multiparametric minimization. This procedure is numerically quite expensive and time-consuming. However, a large number of starting points (\(n ∼ 10^7\) ) is required to achieve a reasonable statistics and resolution in the parameter values. In order to gain a good understanding of the model, its parameter dependence, and parameterization, it is also necessary to perform the fitting also under variation of the physical quantities involved.

##### Neelkamal Mallick [1], Suraj Prasad [1], Aditya Nath Mishra [2,4], Raghunath Sahoo [1] and Gergely Gábor Barnaföldi [3]

[1] Department of Physics, Indian Institute of Technology Indore

[2] Department of Physics, School of Applied Sciences, REVA University

[3] Wigner Research Center for Physics

[4] Department of Physics, University Centre For Research & Development (UCRD), Chandigarh University

**Publication:** Deep learning predicted elliptic flow of identified particles in heavy-ion
collisions at the RHIC and LHC energies

**Abstract** Recent developments of a deep learning feed-forward network for estimating elliptic flow \((v_2)\) coefficients in heavy-ion collisions have shown the prediction power of this technique. The success of the model is mainly the estimation of \(v_2\) from final-state particle kinematic information and learning the centrality and transverse momentum \((p_T)\) dependence of \(v_2\). The deep learning model is trained with Pb-Pb collisions at \(\sqrt{s_{NN}} = 5.02 TeV\) minimum bias events simulated with a multiphase transport model. We extend this work to estimate \(v_2\) for light-flavor identified particles such as \(π^\pm\), \(K^\pm\), and \(p + \bar{p}\) in heavy-ion collisions at RHIC and LHC energies. The number-of-constituent-quark scaling is also shown. The evolution of the \(p_T\)-crossing point of \(v_2(p_T)\), depicting a change in baryon-meson elliptic flow at intermediate \(p_T\) , is studied for various collision systems and energies. The model is further evaluated by training it for different \(p_T\) regions. These results are compared with the available experimental data wherever possible.

#### Mátyás Koniorcyzk [1], Péter Naszvadi [1], Milkós Pintér [2] (2023.04.18 - 07.18)

[1] Wigner Reseach Centre for Physics

[2] Corvinus University of Budapest

**Abstract:** We investigate quadratic binary unconstrained optimization problems (QUBOs) in the framework of the project. These are hard computational tasks equivalent to the Ising model, and can also be solved with quantum annelaers. They can be rewritten into a mixed integer linear problem by the introduction of auxiliary variables; this is the standard (Fortet) linearization. In our research we investigate the extent to which the Fortet linearization can be used to improve on a solution obtained from heuristics (like, e.g. quantum annealing or simulated bifurcation), or the verification of their optimality with duality conditions. Meanwhile we solve small but hard QUBO instances, which contributes also to the better understanding of their structural properties.

##### 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)

**Grant:** NKFIH 2020-2.1.1-ED-2021-00179

**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

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

##### (2022.05.01 - 2022.12.31)

**Grant:** NKFIH 2020-2.1.1-ED-2021-00179

**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

##### 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].

##### 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.

##### 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)