Introduction to Quantum Machine Learning

Abstract

In this tutorial, we will have a first look at variational quantum machine learning (QML), which combines parametrized quantum circuits as models with a classical optimization loop. We start with a brief overview of quantum computing, highlighting its potential as well as its current limitations. Next, using the popular QML framework PennyLane, we show how to build a quantum model and integrate it into standard TensorFlow- or Torch-based ML workflows. Finally, we address pros and cons of QML and discuss open challenges in the field.