Quantum computing is fast emerging as a technology that can potentially disrupt many businesses. BCG predicts that the operating income of quantum computing is likely to reach 2-5B$ by 2024 and up to 50B$ by 2030  . A BurningGlass Technologies survey shows that the demand for quantum computing skills is expected to grow at 135$ in the next 5 years . It is therefore very important to know not only about the technology—what is it, where is it heading and why is it interesting—but also about the programming platform and application domains. In this tutorial, we will introduce Quantum computing and Quantum Machine Learning (QML) technology followed by hands-on session on programming actual Quantum computers. We will focus on quantum machine learning as an application domain and will cover three key algorithms and its applications using Jupyter Notebooks in IBM Quantum Lab platform for demonstration. At the end of the tutorial, we expect the participants will be able to understand the difference between classical and quantum computing and be able to think of potential interplay between Quantum and machine learning to impact both fields. The state-of-art research in quantum machine learning and some of the latest algorithms need to be publicized and practiced in real world for enabling successful translation of QML research. This tutorial intends to address this need and participants would get started on the QML journey by learning quantum programming using Qiskit.
Advisory Research Scientist, IBM
Senior Research Scientist, IBM
Advisory Research Engineer, IBM
Research Scientist, IBM
Senior Engineer, IBM
L. Venkata Subramaniam
STSM, Senior Manager, IBM