This year's Nobel Prize in Physics came as a bit of a surprise to many, as the awarded topic does not seem to relate to the traditional fields of physics for the untrained observer. In this talk, I will try to explain how models rooted in statistical physics have laid the groundwork for the rapidly developing algorithms of artificial intelligence today and give a brief overview of the current...
The seminar will provide a glimpse of some elements of the rapidly evolving field of quantum sensing, specifically focusing on particle physics. Specific approaches involving quantum systems, such as low-dimensional systems or manipulations of ensembles of quantum systems, hold great promise for improving high-energy particle physics detectors, particularly in areas like calorimetry, tracking,...
Variational quantum algorithms are viewed as promising candidates for demon-
strating 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)...
Physics-Informed Neural Networks (PINNs) aim to solve ordinary differential equations (ODEs) and partial differential equations (PDEs). So far, classical digital NNs have been successfully applied to many PDEs, such as the heat equation, Poisson equation, Navier-Stokes equation, to name a few. Recently, Continuous Variable Quantum Neural Networks (CVQNNs) have also been used to solve PDEs in...
With the emergence of quantum programming languages and compilers, the necessity for optimization naturally arises. In 2022, we introduced a new quantum language called Qubla, designed to facilitate the generation of quantum counterparts to classical (binary) algorithms. The Qubla compiler constructs a sequence of quantum operators based on a description of mixed classical/quantum computation,...
Variational quantum algorithms are widely regarded as promising approaches for achieving quantum advantage on near-term hardware. These algorithms typically rely on training parameterized quantum circuits through a classical optimization loop, most often using gradient-based methods with gradients computed via parameter-shift rules. However, in the continuous-variable (CV) model of photonic...
From the HiDALGO2 Centre of Excellence of the EuroHPC, the RedSim native multi-GPU and vectorized multi-CPU code for simulating the compressible Navier-Stokes equations with unstructured polyhedral meshes has been developed and optimized.
From the HiDALGO2 Centre of Excellence of the EuroHPC, the RedSim native multi-GPU and vectorized multi-CPU code for simulating the compressible...
Hadron therapy is a form of cancer therapy, where we aim to destroy those cancerous cells that are hard to reach with surgery. Since this kind of approach differs from the classical gamma radiation therapy, the tomography methods used for that are not sufficient enough for Hadron Therapy . Proton Computed Tomography (pCT) is developed to achieve more accurate results for this kind of...
Predicting whether single amino acid variants (SAVs) in proteins lead to pathogenic outcomes is a critical challenge in molecular biology and precision medicine. Experimental determination of the effects of all possible mutations or those observed in pathogenic individuals is infeasible. While existing state-of-the-art tools such as AlphaMissense show promise, their performance remains...
I will present AI applications in problems from Astrophysics to Robotics. Some of these developments and advancements are done within the Artificial Intelligence National Laboratory (MILAB), a state-of-the-art research facility focused on AI technologies.
In this talk, we present three use cases demonstrating how AI tools are advancing research at the HUN-REN Wigner Research Centre for Physics as part of the AI 4 Science program.
- Literature Review with Large Language Models (LLMs): We leverage large language models to streamline literature searches and process complex scientific documents. Recent state-of-the-art open-source models are...
HUN-REN Cloud's primary goal is to support the Hungarian scientific community by providing essential e-infrastructure for their research. The HUN-REN Cloud is one of the main infrastructure pillars of the AI4Science program, which aims to enhance the use of artificial intelligence within the HUN-REN Research Network. This new program has opened up even more opportunities for researchers on the...
Building models for multi-channel data can be complex, as it is often unclear which channels influence others and what the optimal time lag is. Allowing too many free parameters can be inefficient, requiring large amounts of data for reliable estimation and often leading to a less stable system compared to a model with fewer parameters. This challenge can be addressed by uncovering causal...
WebGPU is a new, upcoming API to harness the power of GPUs. While there are already many competing standards on the market, WebGPU fills a particular gap in the spectrum by enabling applications to launch general compute kernels besides usual vertex and fragment shaders right in your browser. While this already opens up many possibilities for interesting applications, the fact that WebGPU is...
Everything in the Universe rotates. Expanding this principle to the Universe as a whole may seem like a natural step, yet the idea remains unexplored and is mostly dismissed as a curiosity. Recently, however, interest in this topic has gained traction, notably due to the work of Szigeti et el. (2025). In the era of precision cosmology, addressing the growing tensions within the currently...
Everybody knows that machine learning models are initialized with random numbers. If we want to make our experiments reproducible, we have to use a random seed. But when and where should we use random seeds to freeze the entire environment? Sometimes, randomness occurs even when a seed is used.
I present real examples demonstrating the importance of randomness at different levels in machine...
This work investigates how integrating gold nanorods into laser targets enhances laser-driven ion acceleration. By exploiting the localized surface plasmon resonance (LSPR) of gold nanorods, we improve the coupling of femtosecond Ti:Sapphire laser pulses to the target. Numerical simulations reveal that resonant plasmonic excitations in the nanorods substantially intensify local electromagnetic...
Many GPU accelerated applications rely on the cuFFT library for fast and efficient Fourier transform implementations, however for certain algorithms it can be a performance limiting factor due to its strictly host-side API. Library functions cannot be called from code running on the GPU, hence unnecessary kernel launches, and host-device communication can occur when custom operations are...
