Reconstructing quantum states with generative models | TDLS Author Speaking

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=GStjT6Ydpo0


Duration: 1:24:17
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Toronto Deep Learning Series, 17 September 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-09-17/

Paper Review: unpublished

Speaker: https://www.linkedin.com/in/juan-felipe-carrasquilla-alvarez-0973bb6a/
Organizer: https://www.linkedin.com/in/amirfz/

Host: RBC FutureMakers

Paper abstract:
The technological success of machine learning techniques has motivated a research area in the condensed matter physics and quantum information communities, where new tools and conceptual connections between machine learning and many-body physics are rapidly developing. In this talk, I will discuss the use of generative models for learning quantum states. In particular, I will discuss a strategy for learning mixed states through a combination of informationally complete positive-operator valued measures and generative models. In this setting, generative models enable accurate learning of prototypical quantum states of large size directly from measurements mimicking experimental data.



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Tags:
deep learning
quantum machine learning
quantum physics