PIRSA:16060005

Machine Learning with Quantum-Inspired Tensor Networks

APA

(2016). Machine Learning with Quantum-Inspired Tensor Networks. Perimeter Institute for Theoretical Physics. https://pirsa.org/16060005

MLA

Machine Learning with Quantum-Inspired Tensor Networks. Perimeter Institute for Theoretical Physics, Jun. 14, 2016, https://pirsa.org/16060005

BibTex

          @misc{ scivideos_PIRSA:16060005,
            doi = {10.48660/16060005},
            url = {https://pirsa.org/16060005},
            author = {},
            keywords = {Quantum Matter},
            language = {en},
            title = {Machine Learning with Quantum-Inspired Tensor Networks},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2016},
            month = {jun},
            note = {PIRSA:16060005 see, \url{https://scivideos.org/index.php/pirsa/16060005}}
          }
          
Talk numberPIRSA:16060005
Source RepositoryPIRSA
Collection

Abstract

Tensor networks have been very successful for approximating quantum states that would otherwise require exponentially many parameters.

I will discuss how a similar compression can be achieved in models used to machine learn data, such as sets of images, by representing the fitting parameters as a tensor network. The resulting model achieves state-of-the-art performance on standard classification tasks. I will discuss implications for machine learning research, exploring which insights from physics could be imported into this field.