Join us for this exciting QUBIC Seminar Series event.

Advancing Quantum Machine Learning through Algorithm Development and Applications

Speaker: Dr Muhammad Usman, CSIRO Data61
Date: Wednesday 16 October
Zoom: Click here to join the seminar

Bio
Dr Muhammad Usman is a Team Leader and Principal Staff Member at CSIRO’s Data61 which is Australia National Research Organisation. He has over 15 years of research and teaching experience in the field of quantum computing. At CSIRO, Dr Usman is leading a team of over 20 researchers working at the forefront of quantum algorithms, quantum software engineering, and quantum security. He is a fellow of the Australian Institute of Physics and serves on the executive editorial boards of two international journals, a committee member of Standards Australia to help in standardisation of quantum technologies and have Associate Professor affiliations at the University of Melbourne and Monash University. Dr Usman is the chair of the organising committee of International Conference on Quantum Techniques in Machine Learning 2024. His work was nominated as Innovative of the Year 2023 Award by Defence Industry, Winner of the Australian Army Quantum Technology Challenge in three years in a row (2021, 2022 and 2023), Rising Stars in Computational Materials Science by Elsevier in 2020, and Dean’s Award for Excellence in Research (Early Career) at the University of Melbourne in 2019. Dr Usman is a recipient of prestigious international research fellowships from Fulbright USA (20005-2010) and DAAD Germany in 2010. Dr Usman is a passionate quantum educator and has been promoting quantum education among school children as part of the CSIRO’s STEM Scientists in Schools program.

Abstract
The meteoric rise of artificial intelligence in recent years has seen machine learning methods become ubiquitous in modern science, technology, and industry. Concurrently, the emergence of programmable quantum computers, coupled with the expectation that large-scale fault tolerant machines will follow in the near to medium-term future, has led to much speculation about the prospect of quantum machine learning, namely machine learning solutions which take advantage of quantum properties to outperform their classical counterparts. Indeed, quantum machine learning is widely considered as one of the frontrunning use cases for quantum computing. In this talk, I will present our recent research work on addressing some of the fundamental challenges towards practical application of quantum machine learning by developing efficient data encoding techniques, incorporating error mitigation and correction methods, and identifying useful applications of the quantum machine learning models.