The series Lecture Notes in Physics (LNP), founded in 1969, reports new developments in
physics research and teaching-quickly and informally, but with a high quality and the explicit …

… New quantum algorithms may offer tantalizing prospects to enhance machine learning
itself. The interaction between machine learning and quantum physics will undoubtedly benefit …

… sets, training AI for playing Bell nonlocal games, using hidden neurons as hidden variables
for completion of quantum theory, and machine learning-assisted state classification. …

… to the simulation and control of quantum systems. We discuss the … machine learning and
quantum many-body physics. … classical machine learning methods inspired by quantum physics…

… are also shown to have a resolution in the quantum theory. The distributional compositional
categorical (DisCoCat) model of language [Citation94] could be addressed as the first …

… In HEP, owing partially to quantum mechanics, it is often impossible to know the labels of
individual examples from real data. However, simulations (using Monte Carlo methods) play a …

… Specifically, we look at an ample spectrum of fields (ranging from statistical and quantum
physics to high energy and cosmology), where ML recently made a prominent appearance, …

… to take physicists with a background in quantum technologies … at the interface to classical
machine learning. The readers are … learning techniques are applied to quantum physics. We …

… Indeed, we believe that the next breakthroughs in deep learning may come from developing
a solid theoretical foundation, based on concepts and methods from statistical physics. This …

… One particular numerical method originated from quantum physics which has been
increasingly compared to NNs are Tensor Networks (TNs) 14,15,16 . TNs have been developed to …