Characterising and harnessing noise in quantum algorithms & Comparing the Impact of COVID-19 on Student Mathematics Achievement: A Multilevel Machine Learning Analysis of PISA 2022

Speaker: Dr Riddhi Gupta
Affiliation: University of Queensland

Characterising and harnessing noise in quantum algorithms

Abstract: Quantum computing hardware is rapidly advancing to include systems with increasingly greater number of qubits and complex quantum operations to perform logical information processing. However, quantum computation on these systems may also be subject to a greater diversity of noise processes and this degradation may endanger the promise of useful quantum computing. By way of an introduction, I will briefly compare two typical strategies, quantum error correction and quantum error mitigation, that are used to combat the effects of noise on hardware. I will outline how these strategies may fail if intrinsic noise violates underlying assumptions. I will present new tools my team has developed for characterising imperfectly executed error correction and mitigation. I will then discuss our ongoing work on how information learned about noise may be used to improve quantum algorithms, focussing on variational quantum algorithms and sample-based quantum diagonalisation methods (SQD) as specific applications of quantum methods for chemistry. 

Biography: I bring industry and academic experience in working on quantum error mitigation, quantum error correction, and quantum control theory to enable quantum computing demonstrations on near-term hardware. At UQ, my team investigates the feasibility of combining error mitigation and error correction techniques with quantum algorithms. Prior to this, I worked on error mitigation and quantum error correction applications at IBM Quantum (US); classical algorithms for noise filtering and prediction for trapped ions at the Quantum Control Laboratory in the University of Sydney and was the recipient of the ARC EQUS inaugural Director’s Medal in Australia in 2019. 

Speaker: Dr Jinran Wu
Affiliation: University of Queensland

Comparing the Impact of COVID-19 on Student Mathematics Achievement: A Multilevel Machine Learning Analysis of PISA 2022

Abstract: The COVID-19 pandemic led to significant disruptions in schooling worldwide. This study aims to evaluate and compare the impact of the pandemic on student mathematics achievement across eight representative countries/regions, using data from the Programme for International Student Assessment (PISA) 2022. The multilevel random forest (RF) method was employed to account for the effects of national, school, family, and individual contexts, as well as the hierarchical structure of the PISA data. The results show that the multilevel RF model outperformed the traditional multilevel model in terms of predictive accuracy. School closures were found to have the most significant negative impact, while school support and self-directed learning had positive effects. Additionally, the impact of COVID-19 on student mathematics achievement varied substantially across different countries/regions and subgroups. The findings are discussed regarding the resilience of the education system, educational inequities, and their implications for policy and methodology.

Biography: Dr. Wu earned his Ph.D. in Statistics from Queensland University of Technology (QUT), Australia, in 2022. He is currently a postdoctoral research fellow in the School of Mathematics and Physics at The University of Queensland, working with Professor Geoffrey J. McLachlan. Before this appointment, he completed his first postdoctoral training at the Institute for Positive Psychology and Education, Australian Catholic University, and served as an associate lecturer in the School of Mathematical Sciences at QUT. His research focuses on applied statistics and data science, with particular emphasis on interdisciplinary applications in the social sciences and environmental systems.