Quantum Computing for High-Energy Physics and Data Analysis
- Colloquium

Quantum Computing for High-Energy Physics and Data Analysis
Quantum Computing is developing at a tremendous pace, with possibly transformative implications for a wide range of applied and fundamental research areas. After a review of the current state-of-the-art in quantum computing and a brief introduction to some of the most famous quantum computing paradigms, I will discuss concrete examples of how quantum computing algorithms can be beneficially applied to tasks in high-energy physics and data analysis - at current and near-term quantum devices. Examples will include quantum gate computing algorithms for parton showers, quantum machine learning algorithms for classification tasks and the simulation of non-perturbative quantum effects in scalar field theories using quantum spin-lattice systems.



![3D visualisation of human neuronal tissue reconstructed by multi-scale X-ray phase contrast tomography. Neuronal cell nuclei are shown in yellow for the granule neurons in the dentate gyrus region of the hippocampus. Blood vessels are shown in red. By changing the X-ray optical magnification in the multi-scale recordings, one can zoom into regions-of-interest (red ovals). In these scans the resolution is high enough to resolve sub-structures of the nucleus, associated with different DNA packing regimes. Adapted from [6]](/storages/physik/_processed_/e/4/csm_Kolloquium_Salditt_0e30a3f090.png)





