Workshop 4 – Quantum Machine Learning

Description

Abstract:

The area of quantum machine learning is advancing at a rapid pace. This workshop is designed to introduce quantum computing and quantum machine learning to researchers new to this cutting-edge area. This hands-on workshop will start by introducing support vector classifiers
from a classical machine learning perspective and then show how quantum algorithms can be used to enhance their performance. The first part of the workshop will make use of Python in Google Colab, so the attendees are expected to have a Google email account. The second part
will make use of Qiskit, the open-source toolkit by IBM, for useful quantum computing. For the second part of the workshop, the attendees will have to create a free account with IBM Quantum (https://quantum-computing.ibm.com).

At the end of the workshop, the attendees will have a basic understanding of quantum algorithms using IBM Quantum computers and will have run algorithms to improve the performance of classical support vector classifiers using quantum kernels.

Intended audience:
This workshop is ideally suited for researchers who want exposure to quantum machine learning. Some background in Python programming and machine learning will be helpful.

Synopsis:
Given the recent advancements in machine learning, and quantum computing, this workshop brings these two active areas of research together and will equip the researchers to explore their data using novel cutting-edge approaches. Using support vectors-based classification, this workshop will demonstrate how quantum kernels can improve classification accuracy.

Materials:
All material will be provided during the workshop.

Biography:

Alex de Sá
Alex holds a PhD, in 2019, in Computer Science from the Federal University of Minas Gerais (UFMG), Brazil. He is a postdoctoral researcher at the Computational Biology and Clinical Informatics laboratory at the Baker Heart and Diabetes Institute, with an honorary fellowship at the University of Queensland. His research is centered on bio(chem)informatics, health informatics and automated machine learning (AutoML). Particularly, he is interested in automating data science, i.e., employing optimisation methods to select proper machine learning algorithms and models for any dataset of interest. Currently, he is employing this knowledge in chemical, medical and biological data.

Ashar Malik
Ashar completed his PhD, in 2018, in the area of computational biochemistry from Massey University in Auckland, New Zealand and has since been working as a postdoc, first at the Bioinformatics Institute at the Agency for Science, Technology and Research in Singapore andcurrently at Baker Heart and Diabetes Institute, Melbourne. His primary work focuses on exploring polymorphisms in the human genome and their phenotypic effects. Additionally he also explores the area of structural phylogenetics and quantum computing. He is also a science communication enthusiast and loves to engage with incoming researchers on potential new computational methodologies that can be employed to enhance the quality of research. He is also an instructor with Software Carpentry and routinely conducts scientific computing workshops.