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!

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!

July 2023:  A new work from our research assistant Elias building up on our qubit compression algorithms for solving QUBO problems and applications in finance. We solve the transaction settlement problem and boost the performance by almost two orders of magnitude compared to the state of the art!

July 2023:  Another paper was recently put up onto arXiv by PhD student Benjamin and others in the group. We show how the localization landscape can form the basis for hardware-efficient quantum algorithms for solving binary optimization problems, which is competitive with standard QAOA circuits of similar depth!