34 citations · 89 across the 6 of their papers we have counts for
6 papers
Hybrid Quantum Physics-informed Neural Network: Towards Efficient Learning of High-speed Flows
Fong Yew Leong, Wei-Bin Ewe, Tran Si Bui Quang +2
This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and ful…
Solving Fractional Differential Equations on a Quantum Computer: A Variational Approach
Fong Yew Leong, Dax Enshan Koh, Jian Feng Kong +6
We introduce an efficient variational hybrid quantum-classical algorithm designed for solving Caputo time-fractional partial differential equations. Our method employs an iterable…
From Ad-Hoc to Systematic: A Strategy for Imposing General Boundary Conditions in Discretized PDEs in variational quantum algorithm
Dingjie Lu, Zhao Wang, Jun Liu +3
We proposed a general quantum-computing-based algorithm that harnesses the exponential power of noisy intermediate-scale quantum (NISQ) devices in solving partial differential equa…
Variational Quantum Simulation of Partial Differential Equations: Applications in Colloidal Transport
Fong Yew Leong, Dax Enshan Koh, Wei-Bin Ewe +1
We assess the use of variational quantum imaginary time evolution for solving partial differential equations. Our results demonstrate that real-amplitude ansaetze with full circula…
Variational Quantum Evolution Equation Solver
Fong Yew Leong, Wei-Bin Ewe, Dax Enshan Koh
Variational quantum algorithms offer a promising new paradigm for solving partial differential equations on near-term quantum computers. Here, we propose a variational quantum algo…
Variational Quantum-Based Simulation of Waveguide Modes
Wei-Bin Ewe, Dax Enshan Koh, Siong Thye Goh +2
Variational quantum algorithms are one of the most promising methods that can be implemented on noisy intermediate-scale quantum (NISQ) machines to achieve a quantum advantage over…