Synopsis
CERN has started its Quantum Technology Initiative in order to investigate the use of quantum technologies in High Energy Physics (HEP). A three-year roadmap and research programme has been defined in collaboration with the HEP and quantum-technology research communities. In this context, initial pilot projects have been set up at CERN in collaboration with other HEP institutes worldwide on Quantum Computing and Quantum Machine Learning in particular. This talk will provide an overview of recent results obtained by the different studies, focusing on current usage of quantum machine learning techniques for HEP use cases and beyond.
From this talk:
- Complement this note with alternative methods: https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py
- Add threshold decision mechanisms and metrics such as Area under Curve, and precision.
- Add CR gate ( https://www.quantum-inspire.com/kbase/cr-gate/ )
- Bell inequalities No local hidden variables model can represent Quantum Mechanics results.
- McNemar-Bowker Test of Symmetry ( potentially useful webpage )
- Add relevant info from paper https://arxiv.org/abs/2401.10293
- Geometric Quantum Machine Learning: How can we encode physical symmetries in our algorithm?