Welcome to our group website

We are working in quantum technologies with a focus in implementations of quantum computation and quantum simulation with quantum optical systems. Our research could be applied towards developing exotic high-performance quantum processors and simulators, and also for fundamental science in the area of strongly correlated quantum systems. read more.

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.

Research Highlights

June 2023: Qubit efficient quantum algorithms for the vehicle routing problem on quantum computers of the NISQ era

Qubit efficient quantum algorithms for the vehicle routing problem on quantum computers of the NISQ era Ioannis D. Leonidas, Alexander …
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Jan 2023: Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer

Shallow quantum circuits for efficient preparation of Slater determinants and correlated states on a quantum computer Chong Hian Chee, Daniel …
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October 2022: Efficiently Extracting Multi-Point Correlations of a Floquet Thermalized System

Efficiently Extracting Multi-Point Correlations of a Floquet Thermalized System Yong-Guang Zheng, Wei-Yong Zhang, Ying-Chao Shen, An Luo, Ying Liu, Ming-Gen …
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July 2022: Computing Electronic Correlation Energies using Linear Depth Quantum Circuits

Computing Electronic Correlation Energies using Linear Depth Quantum Circuits Chong Hian Chee, Adrian M. Mak, Daniel Leykam, Panagiotis Kl Barkoutsos, …
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June 2022: Topological data analysis and machine learning

Topological data analysis and machine learning Daniel Leykam, Dimitris G. Angelakis arXiv:2206.15075 Topological data analysis refers to approaches for systematically …
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July 2021: Fock State-enhanced Expressivity of Quantum Machine Learning Models

Fock State-enhanced Expressivity of Quantum Machine Learning Models Beng Yee Gan, Daniel Leykam, Dimitris G. Angelakis EPJ Quantum Technology 9 …
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