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 (2023.06.01 - 08.31)

Publication: Analysing high resolution digital Mars images using machine learning

Abstract: The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the HiRISE camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading.

In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40° and -60°. Previously analysed HiRISE images are used to train the model, expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise predictions.

Using a CNN model may make it realistic to analyse all available surface images, aiding us in selecting areas for further investigation.

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

Anna Horváth, Balázs Bámer, Gergely Gábor Barnaföldi and Dávid Légrády (2022.01.01 - 2022.07.30)

Wigner Research Centre for Physics

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

Abstract: We investigate the optical trajectories in non-linear optical medium applying standard description. We apply modern machine learning techniques for the image reconstruction.

Mihály András Pocsai, Imre Ferenc Barna, Gábor Bíró, Gergely Gábor Barnaföldi (2021.10.01 - 2022.08.31)

Wigner Research Centre for Physics

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

Abstract: In the concept of plasma based particle acceleration, the particles are accelerated in the wakefield generated by a driver pulse, which may be either a beam of charged particles or a short, intense laser pulse, instead of guiding,collimating and them in vacuum with strong electromagnetic fields [1]. The witness bunch is usually injected in the plasma from an external source, but in case of electron acceleration using a laser pulse as a driver pulse, at sufficiently high laser intensities, some of the plasma electrons are being trapped in the wakefield generated by the laser pulse. This phenomenon is referred as self-injection. In the schemes mentioned above, the driver bunches transfer their energy to the witness bunches through the plasma waves they generate. In the CERN–AWAKE experiment the wakefield is generated by a train of proton microbunches, produced from the SPS proton beam via the self-modulation instability [2]. In this experiment it is essential for the plasma to be ultrahomogeneous, furthemore, at prescribed points of the plasma, the plasma density has to follow the prescribed density ramps accurately. The plasma itself is produced by photoionising the rubidium vapour with a 120 fs long, intense, infra-red laser pulse. Therefore studying the corresponding photoionisation phenomena is a relevant sub-topic of the CERN–AWAKE experiment.

The processes in question have been already studied earlier via quantum mechanical simulations [3]. In our approach, we expanded the solution of the time-dependent Schrödinger-equaion (TDSE) on the basis of the eigenfunctions of the free Hamiltonian-operator of the Rubidium atom. The expansion coeffitiens are timedependent. Substituting this Ansatz into the TDSE, one obtains a first order, linear ODE system, referred as Coupled Channel Equations. Every channel, i. e. every time-dependent expansion coeffitient gives the occupation amplitude of the corresponding bound or continuum state of the rubidium atom. From the final state wave function, the total photoinisation probabilities, the photoelectron energy spectra, angular distributions and energy-and-angle resolved spectra can be obtained.

References:
[1] T. Tajima, J.M. Dawson: „Laser electron accelerator". Phys. Rev. Lett 43, 267–270 (1979).
[2] C. Petit-Jean-Genaz, G. Arduini, P. Michel, V. R. W. Schaa, (eds.), Proceedings, 5th International Particle Accelerator Conference (IPAC2014): Dresden, Germany, June 15–20, 2014, JACoW Conferences (CERN, Geneva, Switzerland, 2014).
[3] M.A. Pocsai, I.F. Barna and K. Tőkési: „Photoionisation of Rubidium in strong laser fields". Eur. Phys. J. D 73, 74 (2019).

Márk Margóczi, Dávid Légrády (2022.08.01 - 2022.12.31)

Budapest University of Technology and Economics

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

Abstract: Neutron transport calculations of dynamic Monte-Carlo method is emerging new area of research in the nuclear science. The dynamic Monte-Carlo calculation cannot only be used to foretell neutron kinetics, but to enable more complex dynamic simulations, it can also be coupled with thermal-hydraulic codes. The coupled calculation method raises questions of stability and convergence.

GUARDYAN (GPU Assisted Reactor Dynamic Analysis) is a dynamic Monte Carlobased neutron transport code. The GUARDYAN is able to calculate neutron kinetics, but for more complex reactor physics calculations thermal-hydraulic feedback becomes necessary. To achieve the desired calculations the GUARDIAN has been coupled with a SUBCHANFLOW sub-channel flow simulation code. The simplest tool for testing stability and convergence is the stochastic calculus, within the framework of which neutron kinetics can be approximated with a stochastic differential equation. If the variance contribution term of the equation is defined with Monte-Carlo assumptions, then the stochastic differential equation approximates the dynamic Monte-Carlo method simulation. If the problem is sufficiently simple, the neutron kinetics and thermal-hydraulic of the rector can be derived using analytical formulas. In order to calculate expected value, standard deviation and variance with adequate statistical uncertainty corresponding to the method, these equations must be compared with the solutions of practical problems which require numerous simulation.

Application of GPU cluster supports the publication of scientific journal articles, topic of which is the mapping of the relationship between stochastic differential equations and time-dependent Monte-Carlo-based neutron transport and the investigation of the spread of variance of stochastic neutron kinetics to variance of thermal-hydraulic.

Erzsébet Suhajda, Tamás Hegedűs (2022.08.01 - 2022.10.31)

Semmelweis University

Publication: Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology
Poster

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

Abstract: Membrane proteins play crucial roles in cells’ life. They can bind substrates, transport ions, molecules or even drugs in and out of the cell. Their structural role is also important, as they can anchor cells to each other or to different surfaces.

In addition to their function, their structure is also unique. Membrane proteins span the cell membrane, part of their structure is located in the hydrophobic interior of the membrane, forming a transmembrane domain, and other parts are located inside the cell (intracellular) or outside the cell (extracellular). Thus, experimental determination of the structure of transmembrane proteins is a difficult task. For crystallization, membrane proteins must be removed from the membrane bilayer, therefore their native structure is often destroyed, making experimental procedures lengthy, expensive and uncertain.

Therefore, despite their vital role, only about 5% of experimentally resolved protein structures belong to membrane proteins, whereas about 50% of currently marketed drugs act through membrane proteins. Our aim is to investigate the structure and dynamics of transmembrane protein complexes with ATPase activity (e.g. ABC transporters responsible for multidrug resistance in tumor cells or calcium pumps) using both an artificial intelligence (deep learning) based structure determination method (AlphaFold) and molecular dynamics (MD) simulations. Our results will make a contribution to the performance testing of these modelling methods on membrane proteins, to the better understanding of the structures of biologically relevant complexes, and therefore can serve as a basis for future drug developments.

Péter Rakyta, Zoltán Zimborás (2022.01.01 - 2022.06.31)

Eötvös University, Wigner Research Centre for Physics

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

Abstract: In this work we propose a novel numerical approach to decompose general quantum programs in terms of single- and two-qubit quantum gates with a CNOT gate count very close to the current theoretical lower bounds. In particular, it turns out that 15 and 63 CNOT gates are sufficient to decompose a general 3 and 4-qubit unitary, respectively, with high numerical accuracy. Our approach is based on a sequential optimization of parameters related to the single-qubit rotation gates involved in a pre-designed quantum circuit used for the decomposition. In addition, the algorithm can be adopted to sparse inter-qubit connectivity architectures provided by current mid-scale quantum computers, needing only a few additional CNOT gates to be implemented in the resulting quantum circuits.

Publications:
[1] Péter Rakyta, Zoltán Zimborás: Approaching the theoretical limit in quantum gate decomposition, Quantum 6 (2022) 710
DOI: 10.22331/q-2022-05-11-710

Gábor Tolnai, Dávid Légrády (2022.08.01 - 2022.12.31)

Budapest University of Technology and Economics

Publication: Adjoint-based Path Length Stretching in a Woodcock Framework with SIR Angular Biasing

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

Kivonat: The GUARDYAN (GPU Assisted Reactor Dynamic Analysis) code developed at the Budapest University of Technology and Economics Institute of Nuclear Techniques directly models the time-dependent phenomena occurring in nuclear reactors. In contrast to conventional reactor dynamics modelling methods, GUARDYAN applies little to no approximations at simulating the physical processes. Price to pay for ultimate accuracy is running time, a real second translates to 6-24h calculation time depending on the complexity of the reactor geometry.

This project aims at increasing the computation efficiency by applying variance reduction techniques. This is done by the importance (a.k.a. the adjoint) function used for biasing the interaction laws, for which calculation schemes are being developed in the form of nonanalog Woodcock tracking for free path sampling and scouting samples (sampling importance resampling - SIRS ) for the angular bias.

An accurately pre-calculated adjoint function is needed for the proper biasing, this is computed by GUARDYAN specifically for the problem at hand. Large computation effort is needed for producing the sufficiently detailed adjoint for a certain problem, but it can be used for the whole transient scenario. For demonstrating the usefulness of the new variance reduction scheme under development, several test cases of varying complexity should be analysed and the corresponding adjoint function generated demanding large GPU capacity.

Péter Rakyta, Zoltán Zimborás (2022.01.01 - 2022.06.31)

Eötvös University, Wigner Research Centre for Physics

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

Abstract: In this work, we report on a novel quantum gate approximation algorithm based on the application of parametric two-qubit gates in the synthesis process. The utilization of these parametric two-qubit gates in the circuit design allows us to transform the discrete combinatorial problem of circuit synthesis into an optimization problem over continuous variables. The circuit is then compressed by a sequential removal of two-qubit gates from the design, while the remaining building blocks are continuously adapted to the reduced gate structure by iterated learning cycles. We implemented the developed algorithm in the SQUANDER software package and benchmarked it against several state-of-the-art quantum gate synthesis tools. Our numerical experiments revealed outstanding circuit compression capabilities of our compilation algorithm providing the most optimal gate count in the majority of the addressed quantum circuits.

Publications: Efficient quantum gate decomposition via adaptive circuit compression

Kővári Emese, Kovács Tamás, Forgács-Dajka Emese (2022.01.01 - 2022.03.30)

Eötvös Loránd University, Center for Astrophysics and Space Science

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

Abstract: The trans-Neptunian space is of great interest of dynamical studies with an inexhaustible number of intriguing problems to be solved. Our aim is to carry out a large-scale survey of trans-Neptunian objects (TNOs) by means of dynamical maps. In the first part of the research, we concentrate on the dynamical role of mean-motion resonances (MMRs) among the TNOs, and the tools of understanding are dynamical maps of classical chaos indicators. In the second part, our focus becomes the quantification of the chaotic diffusion and that of the stability times of the small bodies. The chaotic diffusion is of fundamental importance for its rate will determine the long-term dynamics of a given celestial system. To estimate the rate of the diffusion (that is, to compute the diffusion coefficients) in the case of the 4125 TNOs selected in the first part of our study, we initiate the use of the Shannon entropy. This latter quantity allows, on the one hand, to measure the extent of unstable regions in the phase space (and thus serves as an indicator of chaos), and also enables the direct measurement of the diffusion coefficients. The characteristic times of stability - in the case of normal diffusion - are then achieved by taking the inverse of the diffusion coefficients. In the knowledge of the chaotic diffusion and stability times for as large a TNO sample as the one indicated above, the overall structure of the trans-Neptunian space might be mapped as well, along with the specification of dynamical classes or the update of the existing ones.

Eduárd Zsurka, Noel Plaszkó, Péter Rakyta, Andor Kormányos (2022.05.01 - 2022.11.31)

Eötvös Loránd University

Publication: Non-local Andreev reflection through Andreev molecular states in graphene Josephson junctions

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

Abstract: We propose that a device composed of two vertically stacked monolayer graphene Josephson junctions can be used for Cooper pair splitting. The hybridization of the Andreev bound states of the two Josephson junction can facilitate non-local transport in this normal-superconductor hybrid structure, which we study by calculating the non-local differential conductance. Assuming that one of the graphene layers is electron and the other is hole doped, we find that the non-local Andreev reflection can dominate the differential conductance of the system. Our setup does not require the precise control of junction length, doping, or superconducting phase difference, which could be an important advantage for experimental realization.

Mira Anna Gergácz, Ákos Keresztúri (2022.08.31-12.31)

Publication: Melting possibility of remnant seasonal water ice patches on Mars

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

Abstract: Due to the low thermal conductivity of the Martian surface and atmosphere, it is possible that after the recession of the seasonal polar icecap, small icy patches left behind in shady places might be met by direct sunlight during the summer. This work surveyed such frost patches using HiRISE images. Analyzing 110 images out of the available 1400 pieces that fit the selection criteria of location and season, and identified 37 images with smaller ice patches on them. These areas range between 140° and 200° solar longitude in the central latitude band between -40° and -60°. The diameter of the ice patches ranges between 1.5-300 meters, and remains on the surface even after the seasonal polar cap has passed over the area for the duration range of 19-133 martian days.

With the help of The Mars Climate Database (MCD) we simulated the surface temperature and predicted CO2 and H2O ice cover at 22 analyzed areas. Judging by the models, the average noon temperature does not reach the melting point of water, which is 273 K, therefore the occurrence of liquid water on the macroscopic scale is highly unlikely, however there is a possibility that an interfacial premelting of ice (a few nanometers thick waterlayer) might form between the layered and the water ice.

Sudár Ákos, Varga-Kőfaragó Mónika, Barnaföldi Gergely Gábor és Légrády Dávid (2021.07.01 - 2022.08.31)

Wigner Research Centre for Physics és BME Institute of Nuclear Techniques

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

Abstract: The goal of development of proton computed tomography is the accurate measurement of the relative stopping power (RSP) distribution of the patient, which is necessary to reduce safety zones around the tumor in proton therapy. During the pCT imaging the patient is imaged by protons, which has determined direction and energy before they go into the patient, and their direction and energy is measured after they come out of the patient. From this information the most likely path (MLP) and the energy deposition in the patient can be determined. The 3D image is reconstructed from the measured data with the use of order suppressed expectation maximalization (OSEM) algorithm, which is an accelerated version of maximum likelihood expectation maximalization (ML-EM) algorithm. The goal of the current project is to develop an image reconstruction code, which runs in parallel threads of CPU and use GPU as well to minimize the image reconstruction time. This software will be used in the future to reconstruct the measured data of a pCT detector developed by the Bergen pCT Collaboration. This work would be contribution to the work of the group and their later publications.

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.