Tensor Networks for Machine Learning (Note venue)

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• Miles Stoudenmire (Flatiron Institute)
• Thursday 26 September 2019, 13:45-15:00
• NOTE venue Watson Building, Lecture Theatre C (G24).

If you have a question about this talk, please contact Mike Gunn.

Tensor networks are a technique originating in quantum many-body physics, which underpin powerful and interesting computational techniques. They can be viewed as a way of storing exponentially big quantum wavefunctions using much fewer parameters. Tensor networks are valued for their ability to be optimized very quickly with adaptive algorithms, and their high degree of interpretability.

If one takes a more general view of tensor networks, they are just a kind of function approximator. This means they could be applied to many interesting problems outside of quantum mechanics too. A particularly interesting setting is modeling real-world, classical data—-machine learning—-where challenges similar to quantum mechanics arise, such as computing joint probabilities of many variables and efficiently computing marginal probabilities.

I will discuss frameworks for applying tensor networks, and tensor network algorithms to perform both supervised and unsupervised learning tasks, and discuss recent progress in this area. One exciting & real possibility is theoretically predicting the outcome of algorithms for training tensor network models based on summary properties of the data.

Another interesting idea is to use quantum circuits identical to tensor networks for machine learning on quantum hardware, allowing transfer of model designs and training methods across these platforms. I will conclude by discussing some future directions where tensor networks could have a significant impact.

This talk is part of the Theoretical Physics Seminars series.

This talk is included in these lists:

• Bham Talks
• NOTE venue Watson Building, Lecture Theatre C (G24)
• Theoretical Physics Seminars
• What's on in Physics?

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