806 citations · 884 across the 11 of their papers we have counts for
11 papers
Bipartitioning of Graph States for Distributed Measurement-Based Quantum Computing
Kjell Fredrik Pettersen, Matthias Heller, Giorgio Sartor +1
Measurement-Based Quantum Computing (MBQC) is inherently well-suited for Distributed Quantum Computing (DQC): once a resource state is prepared and distributed across a network of…
An Exact Branch and Bound Algorithm for the generalized Qubit Mapping Problem
Bjørnar Luteberget, Kjell Fredrik Pettersen, Giorgio Sartor +4
Quantum circuits are typically represented by a (ordered) sequence of gates over a set of virtual qubits. During compilation, the virtual qubits of the gates are assigned to the ph…
A Decomposition Method for the Hybrid Quantum-Classical Solution of the Number Partitioning Problem
Zongji Li, Tobias Seidel, Michael Bortz +1
Current quantum computers can only solve optimization problems of a very limited size. For larger problems, decomposition methods are required in which the original problem is brok…
An Optimization Case Study for solving a Transport Robot Scheduling Problem on Quantum-Hybrid and Quantum-Inspired Hardware
Dominik Leib, Tobias Seidel, Sven Jäger +5
We present a comprehensive case study comparing the performance of D-Waves' quantum-classical hybrid framework, Fujitsu's quantum-inspired digital annealer, and Gurobi's state-of-t…
Representation of binary classification trees with binary features by quantum circuits
Raoul Heese, Patricia Bickert, Astrid Elisa Niederle
We propose a quantum representation of binary classification trees with binary features based on a probabilistic approach. By using the quantum computer as a processor for probabil…
Calibrated simplex-mapping classification
Raoul Heese, Jochen Schmid, Michał Walczak +1
We propose a novel methodology for general multi-class classification in arbitrary feature spaces, which results in a potentially well-calibrated classifier. Calibrated classifiers…
Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification
Raoul Heese, Michael Bortz
We present a novel adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. Our method i…
CupNet -- Pruning a network for geometric data
Raoul Heese, Lukas Morand, Dirk Helm +1
Using data from a simulated cup drawing process, we demonstrate how the inherent geometrical structure of cup meshes can be used to effectively prune an artificial neural network i…
The Good, the Bad and the Ugly: Augmenting a black-box model with expert knowledge
Raoul Heese, Michał Walczak, Lukas Morand +2
We address a non-unique parameter fitting problem in the context of material science. In particular, we propose to resolve ambiguities in parameter space by augmenting a black-box…
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh +11
Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge…
Optimized data exploration applied to the simulation of a chemical process
Raoul Heese, Michal Walczak, Tobias Seidel +2
In complex simulation environments, certain parameter space regions may result in non-convergent or unphysical outcomes. All parameters can therefore be labeled with a binary class…