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.

March 2021: Our new work with Daniel, who joined our group recently, is published at Applied Physics Letters! We merge machine learning with topology and quantum physics for applications at material design. We use a technique known as ‘persistent homology’ to show that it can calculate how nano-structured materials will transmit light. That could make it easier to design man-made photonic crystals, fabricated for example with layers of materials, etched patterns or embedded structures, for specific applications. Check out CQT’s highlight here
December 2020: We have been busy this month. Three of our works are out, two preprints and one publication ! (1) New work out on machine learning with topology (persistent homology) for probing exotic topological bands in condensed matter physics. (2) Our preprint on one on analog quantum machine learning with near term quantum processors is published in Physical Review Research. (3) Our new work on quantum supremacy and quantum phase transition is out as preprint! We propose how our quantum supremacy proposal with driven analog many-body systems can also be used for probing phases of matter.
November 2020: Dimitris was interviewed on Deutsch Welle News Asia on the recent quantum supremacy experiment in China. Check out the interview here.
September 2020: Two new PhD students and a Senior Research Fellow join us. Chee, Beng Yee and Daniel. Welcome guys!
July 2020: New work on qubit efficient algorithms for solving binary optimization problems is out as preprint! We propose and analyze a set of variational quantum algorithms for solving quadratic unconstrained binary optimization problems where a problem consisting of n classical variables can be implemented on O(log(n))number of qubits.
May 2020: Our new work on analog quantum processors for machine learning is out as a preprint! We discuss how the interplay between external driving and disorder in quantum many-body systems can dictate their trainability and expressibility and apply it to solve a generative modelling problem.
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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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