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.

December 2024: We recently submitted a paper onto arXiv introducing a qubit- and shot-efficient method for optimizing cost functions in variational quantum algorithms. Applied to quantum fluid dynamics and ground-state estimation, the approach shows faster and more accurate convergence – congrats to Muhammad Umer and collaborators!
October 2024: Many congratulations to PhD student Chee Chong Hian for successfully defending his thesis on “Resource-Efficient Quantum Algorithms for Quantum Chemistry”! Best of luck for all your future endeavours! You can read more about Chee’s work here.
August 2024: We are excited to share the publication of our latest work building up on our qubit compression algorithms for solving QUBO problems and applications in finance! Using our unique quantum encodings, we solve the transaction settlement problem and managed to boost the performance by almost two orders of magnitude compared to the state of the art! Many congratulations to past research assistant Elias, past PhD student Benjamin, collaborator Paul Griffin from SMU and the SGX Group folks for providing the use case and the relevant data. Read more about our work on the CQT highlight piece!
May 2024:  Another new work was recently put out onto the arXiv by our PhD student Chee and collaborators. In this work, we integrate concepts based on the classical combination of quantum states into existing fault-tolerant algorithms for Hamiltonian simulation. Our proposed hybrid simulator features no optimization subroutines and avoids barren plateau issues, while consuming fewer quantum resources compared to standard methods when many time steps are required.
March 2024:  We are excited to share our latest paper on “Nonlinear Quantum Dynamics in Superconducting NISQ Processors”, a collaborative effort by postdoc Muhammad and others from the Technical University of Crete. We tackle the ground state problem of the nonlinear Schrödinger equation and gain insights into the practical implementation and robustness of QCFD algorithms against hardware-induced noise.
April 2024: Check out our recent paper published to Advanced Quantum Technologies, part of our QEP project in collaboration with ExxonMobil! Using our unique qubit compression quantum optimization algorithms, we solve a route optimization problem, for instances ranging from 11 to 3964 routes constructed with data provided by researchers from ExxonMobil. State of the art so far was maximum 20-30 routes!

Research Highlights

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 …

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 …

Jan 2021: Photonic band structure design using persistent homology

Photonic band structure design using persistent homology D. Leykam, D. G. Angelakis APL Photonics 6, 030802 (2021)  The machine learning …

December 2020: Quantum supremacy and quantum phase transitions

Quantum supremacy and quantum phase transitions S. Thanasilp, J. Tangpanitanon, M. A. Lemonde, N. Dangniam, D. G. Angelakis Phys. Rev …

July 2020: Qubit efficient algorithms for binary optimization problems

Qubit efficient algorithms for binary optimization problems B. Tan, M. A. Lemonde, S. Thanasilp, J. Tangpanitanon, D. G. Angelakis Quantum …

May 2020: Expressibility and trainability of parameterized analog quantum systems for machine learning applications

Expressibility and trainability of parameterized analog quantum systems for machine learning applications J. Tangpanitanon, S. Thanasilp, M. A. Lemonde, N …