Time:  09:30 (Taipei time)
Venue: Online Zoom [Registration] is required
Speaker: ‪Glen Evenbly (Georgia Tech)
Title: Application of tensor networks to data completion and compression.
Abstract: Tensor networks are a set of tools and ideas developed for the efficient study of quantum many-body systems, where they provide a form of entanglement-based representations for quantum states. More fundamentally, however, tensor networks can also be viewed as a form data structure useful for representing certain types of correlated data. They possess many similarities to ideas developed in the context of data science in areas such as data classification, data compression and data completion.

In this talk I will provide an introduction to tensor networks, before describing recent work in which tensor networks are applied to produce more efficient algorithms for image compression (arXiv:220302556) and upcoming work in which tensor-based methods can be applied to the task of data completion; here completing a ground state wavefunction with high fidelity when only starting from a random partial sample.