55 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Fraunhofer Institute for Industrial MathematicsDE5 papers
- University of StuttgartDE5 papers
- Complexity and Topology in Quantum MatterDE2 papers
- Fraunhofer Institute for Manufacturing Engineering and AutomationDE2 papers
- Imperial College LondonGB2 papers
- TRUMPF (Germany)DE2 papers
- University of KaiserslauternDE2 papers
- University of WürzburgDE2 papers
- Zeppelin Universität gemeinnützige GmbHDE2 papers
- Bahria UniversityPK1 paper
- Berlin School of Economics and LawDE1 paper
- City University of Hong KongHK1 paper
7 papers · 1 filter
Neural Network Assisted Fermionic Compression Encoding: A Lossy-QSCI Framework for Resource-Efficient Ground-State Simulations
Yu-cheng Chen, Ronin Wu, M. H. Cheng +1
Quantum computing promises to revolutionize many-body simulations for quantum chemistry, but its potential is constrained by limited qubits and noise in current devices. In this wo…
AutoQML: A Framework for Automated Quantum Machine Learning
Marco Roth, David A. Kreplin, Daniel Basilewitsch +7
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction…
Quantum Neural Networks in Practice: A Comparative Study with Classical Models from Standard Data Sets to Industrial Images
Daniel Basilewitsch, João F. Bravo, Christian Tutschku +1
We compare the performance of randomized classical and quantum neural networks (NNs) as well as classical and quantum-classical hybrid convolutional neural networks (CNNs) for the…
Variational Quantum Algorithms for Differential Equations on a Noisy Quantum Computer
Niclas Schillo, Andreas Sturm
The role of differential equations (DEs) in science and engineering is of paramount importance, as they provide the mathematical framework for a multitude of natural phenomena. Sin…
Training robust and generalizable quantum models
Julian Berberich, Daniel Fink, Daniel Pranjić +2
Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machin…
Optimal Particle-Conserved Linear Encoding for Practical Fermionic Simulation
M. H. Cheng, Yu-Cheng Chen, Qian Wang +4
Number-conserved subspace encoding reduces resources needed for quantum simulations, but scalable complexity trade-off bounds for modes and particles with $\mathcal{O}(N\lo…
Symmetry-based quantum algorithms for open-shop scheduling with hard constraints
Lennart Binkowski, Gereon Koßmann, Christian Tutschku +1
Encoding hard-constrained optimization problems into a variational quantum algorithm often turns out to be a challenging task. In this work, we provide a solution for the class of…