May 2023:  Our group leader Dimitris, PhD student Chee, and collaborator Adrian Mak recently attended the QUANTUMatter 2023 conference in Madrid, Spain, giving talks on both quantum chemistry and quantum optimization. Check out the exciting program here.

April 2023:  The first paper of our PhD student Harvey is out! We studied how topological data analysis can be used to characterize complex chaotic quantum dynamics! Well done Harvey and team!

March 2023:  Supercomputing Asia, a massive gathering of HPC experts from all over the region is on! Our group leader has been giving two talks on qubit efficient quantum algorithms for chemistry and optimization and their applications to industrial use cases. Check out the exciting program here .

February 2023:  Our paper on a sampling quantum advantage with analog quantum many-body systems is published in Quantum Science and Technology! A first effort in merging complexity theory and thermalization physics! In parallel, our review of applications of topological data analysis to physics and machine learning problems in physics is accepted by Adv. Phys. X!

January 2023:  Our new work on quantum computing for chemistry is out! We propose an efficient quantum circuit for second quantized fermionic ansatz state preparation for quantum chemistry and material applications. Our results are particularly timely given the recent consensus that more breakthroughs are required to resolve the quantum state preparation bottleneck that prohibits a quantum advantage for large scale quantum chemistry problem.

December 2022:  Harvey’s first preprint is out! Topological data analysis is a powerful framework for extracting useful topological information from complex datasets. Using concepts from persistent homology we show how unravelling aspects quantum chaos is possible. We apply our findings to specific examples of open quantum systems implementable in NISQ devices.