Quantum computing provides a novel approach to generating true random numbers by leveraging the inherent unpredictability of quantum mechanics. In this method, quantum states, such as superposition, are manipulated using gates like the Hadamard gate, which places a qubit in a state where it has an equal probability of being measured as 0 or 1. Upon measurement, the qubit collapses into either 0 or 1 with no determinable pattern, making the outcome genuinely random. When implemented on real quantum hardware, this process taps into quantum phenomena beyond classical randomness, producing sequences of zeros and ones with high unpredictability. Quantum random number generators (QRNG) are thus considered more secure and reliable for cryptographic and scientific applications compared to classical methods.

Continuum Damage Mechanics (CDM) is a pivotal field bridging materials science and applied physics, offering a framework to predict and analyze material degradation under various loading conditions. This seminar will explore the fundamental principles of CDM, focusing on the progressive damage mechanisms that lead to material failure, including microcrack initiation, void growth, and delamination. The presentation will highlight the mathematical modeling of damage evolution, leveraging tensorial approaches to describe stress-strain relationships in damaged materials. Emphasis will be placed on applications within engineering and physics, from enhancing the durability of structural components to advancing computational models for failure prediction. By integrating theoretical concepts with practical examples, this seminar aims to demonstrate the relevance of CDM in addressing real-world challenges in material design and analysis.

Deep learning has become an essential instrument in the field of particle physics, revolutionising the way in which scientists analyse vast amounts of complex data derived from experiments. This seminar explores how neural networks, specifically generative models and convolutional neural networks (CNNs), are used in high-energy physics. At major research facilities, such as the Large Hadron Collider at CERN, deep learning is employed to perform tasks such as event classification, particle differentiation, and the identification of rare or unexpected events that may indicate the existence of new physics beyond the Standard Model. Furthermore, generative models such as GANs speed up particle interaction simulations, lowering computational costs.