Biricz, András (2025.11.01 - 2026.04.30)

Abstract: In this study, we developed a modular and scalable pipeline for the automated generation of high-quality object detection datasets from large-scale Whole Slide Images (WSIs), with a particular focus on pollen microscopy. The framework leverages recent advances in open-vocabulary vision-language models (e.g., OWL-ViT) for one-shot object detection, further refined through vision transformer embeddings (DINOv2) and distribution-aware clustering techniques.

To ensure reliable training data, we adopted an active learning paradigm, where only clean, high-confidence detections were used to fine-tune specialized Faster R-CNN models with ViT backbones. These models were then deployed in a refinement loop to recover additional detections and improve coverage. The pipeline is generalizable to other domains, such as medical histology or cellular imaging.

High-performance GPU resources were essential for processing high-resolution WSIs, computing embedding representations for hundreds of thousands of image crops, and training large-scale vision transformer models.

Legeza, Örs (2025.11.01 - 2026.04.30)

Abstract: The numerical simulation of quantum systems characterized by strong interactions between atomic spins or mobile electrons, beyond the scope of perturbation methods, is a fundamental aspect of contemporary physics. However, this presents a significant challenge due to the exponential scaling of computational resources with system size. Consequently, the development of algorithms capable of reducing this complexity to a polynomial form constitutes one of the most actively researched domains within the scientific community today.

The density matrix renormalization group (DMRG) algorithm is one such method that offers an added advantage. It divides the relevant tensor algebra into millions of independent subtasks based on the remaining quantum numbers. This makes it an ideal environment for MPI and GPU-based massive parallelization. In 2021-2024, we have already conducted simulations on various quantum systems using our hybrid CPU and multiple GPU-accelerated applications. As a result, we have published five articles and a handbook in Phys Rev B, J. Chem Theory and Comput, and arXiv.

In the proposed research program, we aim to investigate systems containing novel metal atoms that are inaccessible through conventional quantum chemistry methods. Furthermore, we intend to precisely determine the electronic structure of intermediate states generated during novel catalytic reactions. Additionally, we plan to conduct simulations of the static and dynamic properties of strongly correlated two-dimensional fermion lattice models.

Requirements: To conduct the tests, we require a dedicated node equipped with a local disk. During the test, access to all eight GPU cards is essential, and only our application should be executed. Failure to adhere to these requirements will result in unreliable test results. Similarly, our program utilizes the full system capacity of the GPU node. Consequently, no other jobs can be executed simultaneously to avoid conflicts in memory management and GPU utilization.

Franciska Sprok, Edit Fenyvesi, Gábor Gyula Kiss, Dénes Molnár, Gergely Gábor Barnaföldi (2025.10.01 - 2025.12.31)

Abstract: The consistency of nuclear decay rates can be investigated through long-duration precision measurements. Whether long-term or short-term fluctuations occur remains an open question. Our project aims to identify the presence of such modulations, with particular focus on annual variations. We measure the decay rate of a 137-Cs source at the Jánossy Underground Research Laboratory using a High-Purity Germanium (HPGe) detector. Gamma-ray spectroscopy is employed to analyze the source, collecting hourly counts over several months to allow identification of both short-term and long-term deviations in the decay rate. Within this project we analyze the huge amount of collected data using massively parallel computational techniques.

Aswathy Menon Kavumpadikkal Radhakrishnan, Suraj Prasad, Neelkamal Mallick, Gergely Gábor Barnaföldi (2025.09.01 - 2025.12.31)

Abstract: The nuclear structure of Oxygen and Neon can be considered within the nuclear cluster models, using He (or alpha) building block. The properties of this structure can by tested via high-energy heavy-ion collisions, measuring the flow harmonics.

Szabolcs Molnár, Gábor Bíró , Gábor Papp, Gergely Gábor Barnaföldi (2025.08.01-2025.12.31)

Abstract: The parameter fitting and tuning of the HIJING++ colde using machine-learning-based methods within the HEPMC3 framework.

Gábor Bíró, László Dobos (2024.07.01-09.30)

Abstract: Environmental sound sample analysis using artificial intelligence methods for applied research.

Mátyás Koniorczyk (2025.09.01. - 2026.03.31)

Abstract: The recent development of quantum optimization hardware, such as e.g. quantum annealers, has directed an interdisciplinary research attention to quadratic binary optimization problems. These are broadly studied in the operations research literature and also in physics where they are known as Ising spin glass systems. The goal of the present project is to deepen the structural understanding of such problems by solving benchmark and practical instances using solvers developed by our collaborators: BiqBin, a classical exact solver and SpinGlassPEPS.jl, a recent tensor-network-based heuristic, and orchestrating these two. We plan to contribute to the development to these solvers. The addressed problems range from our recently introduced code-theoretic benchmark candidates to railway optimization applications.

Bence Bakó (2025.03.01-09.30)

Abstract: Quantum generative learning offers immense potential as the natural machine learning application of quantum computers, but it faces several trainability challenges. However, certain restricted and structured quantum generative models have a potential to overcome these trainability issues, while allowing the efficient classical estimation of the expectation values and gradients of local observables. These estimates can be used to train the quantum model completely classically, thus also eliminating the need for quantum gradient computation. Furthermore, some classes of quantum models, such as IQP or matchgate circuits, enable this type of classical training, while requiring a quantum device for efficient sampling. In this work, we study such restricted and structured quantum generative models that maintain classical trainability while having a potential for quantum advantage in sampling.

Zoltán Kolarovszki (2025.03.01 - 09.30)

Abstract: Piquasso, an open-source quantum computing framework, enables fast and scalable simulations of photonic quantum circuits. It provides a high-level Python programming interface for efficient simulation of both discrete and continuous-variable photonic quantum computing. By integrating optimized numerical methods, high-performance C++ implementations, and machine learning frameworks, Piquasso addresses the computational intensity of quantum simulations. Leveraging HPC resources allows researchers to explore quantum advantage schemes, test error mitigation strategies, and benchmark experimental results against idealized models. As photonic quantum hardware continues to evolve, robust simulation tools powered by HPC infrastructure will remain essential for bridging the gap between theoretical models and real-world quantum computing applications.

Péter Rakyta (2025.03.01 - 9.30)

Abstract: Variational quantum algorithms are viewed as promising candidates for demonstrating quantum advantage on near-term devices. These approaches typically involve the training of parameterized quantum circuits through a classical optimization loop. However, they often encounter challenges attributed to the exponentially diminishing gradient components, known as the barren plateau (BP) problem. In our work we introduce a novel optimization approach designed to alleviate the adverse effects of BPs during circuit training. In contrast to conventional gradient descent methods with a small learning parameter, our approach relies on making a finite hops along the search direction determined on a randomly chosen subsets of the free parameters. The optimization search direction, together with the range of the search, is determined by the distant features of the cost-function landscape.We have successfully applied our optimization strategy to quantum circuits comprising 16 qubits and 15000 entangling gates, demonstrating robust resistance against BPs.

Anna Horváth, Aneta Magdalena Wojnar, Gergely Gábor Barnaföldi (2024.12.01 - 2025.03.31)

Abstract: We investigate the behaviour of massive and massless particles in strong gravitational field, with one extra spatial compactified dimension. We study a Schwarzschild-like solution in the Kaluza-Klein model, and the possible modifications to observables in general relativity. Curvature and the uncertainty relation could be modified, leading to an altered thermodynamics.