The Optimal Depth of Variational Quantum Algorithms Is QCMA-Hard to Approximate
L. Bittel, S. Gharibian, M. Kliesch, in: Proceedings of the 38th Computational Complexity Conference (CCC), 2023, p. 34:1-34:24.

On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling
S. Pukrop, On Cloud Assisted, Restricted, and Reosurce Constrained Scheduling, 2023.

Utilizing Redundancy in Distributed Heterogeneous Storage
F. Schneider, Utilizing Redundancy in Distributed Heterogeneous Storage, 2023.

Reconfigurable Random Forest Implementation on FPGA
M. Raeisi Nafchi, Reconfigurable Random Forest Implementation on FPGA, 2023.

Graph Neural Network-based Anomaly Detection in  Smart Grid Energy Consumption
A. Ranade, Graph Neural Network-Based Anomaly Detection in  Smart Grid Energy Consumption, 2023.

Highly accurate deep compressed facial recognition
O. Ali, Highly Accurate Deep Compressed Facial Recognition, 2023.

Virtual On-Demand Volunteer System Based on Delaunay Triangulation
N. Ashri, Virtual On-Demand Volunteer System Based on Delaunay Triangulation, 2023.

Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and Circuit-Switched Inter-FPGA Networks
M. Meyer, T. Kenter, C. Plessl, ACM Transactions on Reconfigurable Technology and Systems (2023).

Mutation Tree Reconstruction of Tumor Cells on FPGAs Using a Bit-Level Matrix Representation
J.-O. Opdenhövel, C. Plessl, T. Kenter, in: Proceedings of the 13th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, ACM, 2023.

Improving the End-of-Line Test of Custom-Built Geared Motors using Clustering based on Neural Networks
N. N., Improving the End-of-Line Test of Custom-Built Geared Motors Using Clustering Based on Neural Networks, 2023.

Neuro-Symbolic Class Expression Learning
C. Demir, A.-C. Ngonga Ngomo, International Joint Conference on Artificial Intelligence (2023).

Computing and Compressing Electron Repulsion Integrals on FPGAs
X. Wu, T. Kenter, R. Schade, T. Kühne, C. Plessl, in: 2023 IEEE 31st Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 2023, pp. 162–173.

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics
R. Schade, T. Kenter, H. Elgabarty, M. Lass, T. Kühne, C. Plessl, The International Journal of High Performance Computing Applications (2023).

Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions
A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions, 2023.

IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran
R.H. Gusmita, A.F. Firmansyah, D. Moussallem, A.-C. Ngonga Ngomo, in: Natural Language Processing and Information Systems, Springer Nature Switzerland, Cham, 2023.

Accelerating Concept Learning via Sampling
A. Baci, S. Heindorf, in: CIKM, 2023.

Partial observations, coarse graining and equivariance in Koopman  operator theory for large-scale dynamical systems
S. Peitz, H. Harder, F. Nüske, F. Philipp, M. Schaller, K. Worthmann, ArXiv:2307.15325 (2023).

Finite-data error bounds for Koopman-based prediction and control
F. Nüske, S. Peitz, F. Philipp, M. Schaller, K. Worthmann, Journal of Nonlinear Science 33 (2023).

Multi-Objective Optimization for Sparse Deep Neural Network Training
S.S. Hotegni, S. Peitz, M.B. Berkemeier, ArXiv:2308.12243 (2023).

Efficient time stepping for numerical integration using reinforcement  learning
M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz, K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) 9–5.