6 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
QuAS: Quantum Application Score for benchmarking the utility of quantum computers
Koen J. Mesman, Ward van der Schoot, Matthias Möller +1
Benchmarking quantum computers helps to quantify them and bringing the technology to the market. Various application-level metrics exist to benchmark a quantum device at an applica…
Q-Profile: Profiling Tool for Quantum Control Stacks applied to the Quantum Approximate Optimization Algorithm
Koen J. Mesman, Francesco Battistel, Edgar Reehuis +5
Scaling up the number of qubits and speeding up the execution of quantum algorithms are important steps towards reaching quantum advantage. This poses heavy demands particularly on…
QPack Scores: Quantitative performance metrics for application-oriented quantum computer benchmarking
Huub Donkers, Koen Mesman, Zaid Al-Ars +1
This paper presents the benchmark score definitions of QPack, an application-oriented cross-platform benchmarking suite for quantum computers and simulators, which makes use of sca…
QPack: Quantum Approximate Optimization Algorithms as universal benchmark for quantum computers
Koen Mesman, Zaid Al-Ars, Matthias Möller
In this paper, we present QPack, a universal benchmark for Noisy Intermediate-Scale Quantum (NISQ) computers based on Quantum Approximate Optimization Algorithms (QAOA). Unlike oth…