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.

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!

Jan 2024:  Another new work was recently put out onto the arXiv by our PhD student Harvey, who shows how dimensionality reduction techniques from machine learning can be used to distinguish between thermal and non-thermal phases in quantum many-body scar systems!