23 citations · 25 across the 8 of their papers we have counts for
9 papers
Illuminating early ZrC oxidation using ensemble small-cell DFT with dynamic gas management
Philip Wurzner, Boyang Chen, Matthias Möller +3
During zirconium carbide oxidation, liberated carbon can remain beneath the oxide or escape as gas, complicating the interpretation of mass measurements. We adapt the small-cell en…
MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting
Koen Mesman, Yinglu Tang, Matthias Moller +2
Current quantum computers do not yet have the required qubit resources to meet the demands of most practical quantum algorithms. To circumvent this constraint, the practice of divi…
Quantum Lattice Boltzmann with Denoising Collision Operators
Trong Duong, Matthias Möller, Norbert Hosters
The Lattice Boltzmann method (LBM) is a well-established mesoscopic approach for simulating fluid dynamics by evolving particle distribution functions on discrete lattices. While t…
Performance Comparison of Gate-Based and Adiabatic Quantum Computing for AC Power Flow Problem
Zeynab Kaseb, Matthias Moller, Peter Palensky +1
We present the first direct comparison between gate-based quantum computing (GQC) and adiabatic quantum computing (AQC) paradigms for solving the AC power flow (PF) equations. The…
Power flow and optimal power flow using quantum and digital annealers: a computational scalability analysis
Zeynab Kaseb, Matthias Moller, Pedro P. Vergara +1
This study further explores reformulating power flow (PF) analysis as a discrete combinatorial optimization problem, proposed in our earlier study using the Adiabatic Quantum Power…
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems
Zeynab Kaseb, Rahul Rane, Aleksandra Lekic +4
Quantum computing has emerged as a promising computational paradigm to address unresolved challenges in the modeling and control of modern power systems. However, most existing stu…
A Framework for Solving Continuous Energy and Power System Problems using Adiabatic Quantum Computing
Zeynab Kaseb, Matthias Moller, Peter Palensky +1
The increasing scale and nonlinearity of modern energy and power system problems pose significant challenges to classical numerical solvers. In parallel, advances in quantum and qu…
NN-AE-VQE: Neural network parameter prediction on autoencoded variational quantum eigensolvers
Koen Mesman, Yinglu Tang, Matthias Moller +2
A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such…
Quantum Neural Networks for Power Flow Analysis
Zeynab Kaseb, Matthias Moller, Giorgio Tosti Balducci +2
This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two datasets based on…