Physics and Machine Learning - Emerging Paradigms

Current research in Machine Learning (ML) combines the study of varia-
tions on well-established methods with cutting-edge breakthroughs based
on completely new approaches. Among the latter, emerging paradigms
from Physics have taken special relevance in recent years. Although still
in its initial stages, Quantum Machine Learning (QML) shows promising
ways to speed up some of the costly ML calculations with a similar or
even better performance than existing approaches. Two additional ad-
vantages are related to the intrinsic probabilistic approach of QML, since
quantum states are genuinely probabilistic, and to the capability of nding
the global optimum of a given cost function by means of adiabatic quan-
tum optimization, thus circumventing the usual problem of local minima.
Another Physics approach for ML comes from Statistical Physics and is
linked to Information theory in supervised and semi-supervised learning
frameworks. On the other hand, and from the perspective of Physics, ML
can provide solutions by extracting knowledge from huge amounts of data,
as it is common in many experiments in the eld, such as those related to
High Energy Physics for elementary-particle research and Observational

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