## 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.

## Details

**Talk Number**PIRSA:20010095

**Speaker Profile**Mat Kallada

- Condensed Matter

**Scientific Area**

- Scientific Series

**Talk Type**