PIRSA:20010095

Meta-Learning Algorithms and their Applications to Quantum Computing

APA

Kallada, M. (2020). Meta-Learning Algorithms and their Applications to Quantum Computing. Perimeter Institute. https://pirsa.org/20010095

MLA

Kallada, Mat. Meta-Learning Algorithms and their Applications to Quantum Computing. Perimeter Institute, Jan. 28, 2020, https://pirsa.org/20010095

BibTex

          @misc{ pirsa_20010095,
            doi = {},
            url = {https://pirsa.org/20010095},
            author = {Kallada, Mat},
            keywords = {Condensed Matter},
            language = {en},
            title = {Meta-Learning Algorithms and their Applications to Quantum Computing},
            publisher = {Perimeter Institute},
            year = {2020},
            month = {jan},
            note = {PIRSA:20010095 see, \url{https://pirsa.org}}
          }
          

Abstract

Meta-learning involves learning mathematical devices using problem instances as training data. In this talk, we first describe recent meta-learning approaches involving the learning of objects such as: initial weights, parameterized losses, hyper-parameter search strategies, and samplers. We then discuss learned optimizers in further detail and their applications towards optimizing variational circuits. This talk also covers some lessons learned starting a spin-off from academia.