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, Emese Forgács-Dajka, Gergely Gábor Barnaföldi (2024.12.01 - 2025.03.31)

Abstract: Compact stars in the Kaluza-Klein space-time are investigated, with multiple additional compactified spatial dimensions (d). Within the extended phenomenological model, a static, spherically symmetric solution is considered, with the equation of state provided by a zero temperature, interacting multi-dimensional Fermi gas. The maximal masses of compact stars are calculated for different model parameters. We investigate the effect of the existence of multiple extra compactified dimensions within the Kaluza--Klein compact star structure. We investigate the effect of the number of extra dimensions in comparison with the effect of the excitation number.

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.

Antal Jakovác, Anna Horváth, Bence Dudás (2024.07.01-09.30)

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

Szabó, Vencel (ELTE); Barbola, Milán Gábor (ELTE); Méhes, Máté (ELTE); Gábor Papp (ELTE), Bíró, Gábor (Wigner); Jólesz, Zsófia (ELTE-Wigner); Dudás, Bence (ELTE-Wigner) (2024.03.01 - 2024.06.30)

Abstract: Proton Computer Tomography (pCT) differs from the "normal" photon-based CT, since the basic reaction with matter differs: while in pCT the small angle Coulomb scattering is the dominant process, in (photon) CT the incoming photon is absorbed. That makes pCT a much harder problem.

During the project the students generate input data for the pCT algorithm, running massively the GATE simulation software on different phantoms. Evaluating the inputs with the Richardson-Lucy algorithm we determine the number of runs at different positions and angles to obtain az accetable resolution of the image. Futher plans involve the optimization of the Richardson-Lucy algorithm on GPU cluster to speed up the calculations. Furthermore, they also try to reconstruct the pCT input data from the detector outputs.