Speaker
Description
In this presentation you'll see how to use TensorFlow Quantum to conduct large scale research in QML. The presentation will be broken down into two major sections: First you will follow along as we implement and scale up (beyond the authors original size) some existing QML works from the literature in TensorFlow Quantum. We will focus on how to write effective TensorFlow Quantum code, visualization tools and surrounding software that the TensorFlow ecosystem has curated that can be leveraged for QML. In the second half of the presentation we will review our recent work titled "Power of data in quantum machine learning" (https://arxiv.org/abs/2011.01938) and why we think developing an understanding of data is an important step to achieving quantum advantage in QML.
