Combining Machine Learning and Research in Quantum Foundations: A Brief Survey

Nancy J. Delong

In a research paper not too long ago printed in an on line scientific journal AVS Quantum Science, the authors current an overview of the most modern works discussing achievable purposes of machine learning in the area of quantum foundations.

Quantum fluctuations – artistic strategy. Impression credit: (free of charge licence)

Quantum foundations, as a scientific self-discipline, aims to mathematically make clear the underlying laws of quantum concept that are pretty normally counter-intuitive to our human logic and also with no likelihood to apply concepts of bodily instinct. In get to achieve this intention, researchers normally reformulate principles or even propose new generalizations in get to conquer this conceptual hole and to uncover realistic genuine-entire world purposes.

Below, the authors go over suggestions suggested by numerous researchers of machine learning that have efficiently been applied to resolving different difficulties in quantum foundations, and also current their own insights into achievable foreseeable future research eventualities.

Driven by the success of machine learning in Bell nonlocality, it is real to question if the techniques could be practical to resolve difficulties in quantum steering and contextuality. Not long ago, suggestions from the exclusivity graph solution to contextuality ended up used to look into difficulties involving causal inference. Suggestions from quantum foundations could even further support in establishing a deeper knowledge of machine learning or in general synthetic intelligence.

Exploration posting: “Machine learning satisfies quantum foundations: A quick study,” by Kishor Bharti, Tobias Haug, Vlatko Vedral, and Leong-Chuan Kwek, AVS Quantum Science (2020). The posting can be accessed at


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