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Quantum Machine Learning concepts and applications
Javier Orduz

We explore Machine Learning techniques and Quantum Computing concepts that could be applied in High Energy Physics considering a phenomenological and theoretical view. In this framework, we show the main tools to explore the Standard Model extensions, decay process and the parameter space. With this set of tools, we want to explore the bounds and define exclusion regions, this results might be interesting for the next generation of colliders and could prove to be useful in the understanding of phenomena.

Author Information

Javier Orduz (UNAM)

Dr. Javier Orduz is a Research Scholar and instructor of Computer Science at Baylor University in Texas. He worked in UNAM (Mexico) for five years as a Postdoctoral and Associate Professor before arriving in the USA and work with Dr. Erich Baker and Dr. Pablo Rivas. He also coordinates QMexico (QMexico), an academic community interested in promoting Quantum Computing in Latin America. He participates as Research Scientist in Baylor AI, a group led by Dr. Rivas (Baylor AI Lab), is a mentor in LatinX community LatinX AI, and has experience teaching and researching Quantum Machine Learning, Quantum Computing, Machine Learning, and High Energy Physics. Dr. Orduz loves Mathematics, Physics, and Computing, and he promotes science in Latin America with a high interest in technology.