Ultrafast dynamics is among the most revolutionary topics of modern physical science, and it links up photochemistry with materials research and basic quantum study/hot quantum technology. The dynamics of electrons, spins and nuclei that determine the microscopic fundamentals of energy conversion, information transfer and structural change is observable for researchers through ongoing probing (and manipulation) of matter on timescales from femto (10⁻¹⁵ s ) to attoseconds (10⁻¹⁸ s).

Quantum computing is one of the most innovative technological advancements in the 21st century. Quantum computers leverage the principles of superposition, entanglement or quantum interference, and can perform computations at exponentially faster rates than classical computers do with just binary bits. Major breakthroughs in machine learning, optimisation, cryptography and molecular simulation that are computationally intractable using classical computers are expected to stem from this paradigm shift.

Quantum mechanics, a subject that was once the province of only basic physics but which is now causing a major upheaval in computing. In this paper will be discussed how can quantum-physical principles, such as interference, entanglement and superposition are useful in order to try to build a new model for processing (quantum computing).

The exponential growth of digital humanities databases in the past several years have introduced behemoth accumulations of linguistic, cultural and historical content that are for all practical purposes only accessible by highly trained experts, though we imagine them to be theoretically accessible to anyone.

The development of language, imagery, music and multimodal artefacts has already been disrupted by the swift progress of generative artificial intelligence (AI), leading to increasing confusion between algorithmic synthesis and human creativity. The interpretive, historical and cultural dimensions that the humanities have long studied are typically overlooked by engineering-centric research in generative models, which has focused heavily on optimisation, scale and benchmark performance.

The exponential increase of generative artificial intelligence (AI) has introduced a new paradigm in the way we conceive, model and validate engineering systems. AI technologies are commonly added as helper appendices to good old computer-aided design (CAD) and system engineering workflows, which remain largely deterministic automated and manual despite strong organisation and rich in tools.

On the one side, engineering design is taking on a new dimension as artificial intelligence (AI) and self-governing software agents are beginning to augment and automate intricate creative, analytical and optimisation routines. Traditional infrastructure engineering and CAD methods are still predominantly manual iterative simulation, integrating experts’ reasoning, heuristic improvement.