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Previous Programs

2019 Intelligence in the Physical World
Awardees

2019 Information as Fuel
Awardees

2018 Agency in the Physical World
Awardees; RFP download

2016 Physics of the Observer
Awardees; RFP download

2015 The Physics of What Happens
Awardees; RFP download

2013 Physics of Information
Awardees; RFP download

2010 The Nature of Time
Awardees; RFP download

2008 Foundational Questions in Physics and Cosmology
Awardees; RFP download

2006 Foundational Questions in Physics and Cosmology
Awardees; RFP download

Cristhiano Andre Gamarano Duarte Carneiro Silva
Chapman University

Co-Investigators

Matthew Leifer, Chapman University

Project Title

Reasoning in a Quantum World

Project Summary

Machine learning algorithms based on deep learning have achieved impressive feats in recent years, and are having a large effect on our daily life in applications from facial recognition to determining the news feed on Facebook. Many machine learning algorithms are based on the reasoning in the face of uncertainty using the tools of probability and statistics. However, the world we live in is fundamentally quantum mechanical, and quantum systems obey a different set of rules from those described by ordinary probability theory. Therefore, if we want machines to learn about the world we actually live in, then we need a different set of techniques and algorithms to reason about the quantum world. In this proposal, we ask whether conventional machine learning algorithms can learn about the difference between the classical and quantum worlds, how to reason about cause and effect in quantum theory, and whether a quantum agent (i.e. an entity with a quantum computer for a brain) would describe the world differently than us. Apart from improving our foundational understanding, we expect this to have applications in quantum computing and information.



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