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Generative models, rare events and tensor networks

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

I will consider the integration of some ideas and methods from non-equilibrium statistical mechanics and many-body physics with problems in machine learning, specifically so-called diffusion models and reinforcement learning. The focus will be on the optimal realisation of rare events, and on the use of tensor networks to efficiently represent states and dynamical operators. I will also extend some of these ideas to quantum stochastic dynamics, discussing similarities and differences between the classical and quantum cases.

This talk is part of the Theory of Condensed Matter series.

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