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.
Back to List of Awardees