1 citations · 1 across the 5 of their papers we have counts for
6 papers
Evolutionary-Based Circuit Optimization for Distributed Quantum Computing
Leo Sünkel, Jonas Stein, Gerhard Stenzel +3
In this work, we evaluate an evolutionary algorithm (EA) to optimize a given circuit in such a way that it reduces the required communication when executed in the Distributed Quant…
Quality Diversity for Variational Quantum Circuit Optimization
Maximilian Zorn, Jonas Stein, Maximilian Balthasar Mansky +3
Optimizing the architecture of variational quantum circuits (VQCs) is crucial for advancing quantum computing (QC) towards practical applications. Current methods range from static…
Evaluating Parameter-Based Training Performance of Neural Networks and Variational Quantum Circuits
Michael Kölle, Alexander Feist, Jonas Stein +2
In recent years, neural networks (NNs) have driven significant advances in machine learning. However, as tasks grow more complex, NNs often require large numbers of trainable param…
Accelerated VQE: Parameter Recycling for Similar Recurring Problem Instances
Tobias Rohe, Maximilian Balthasar Mansky, Michael Kölle +3
Training the Variational Quantum Eigensolver (VQE) is a task that requires substantial compute. We propose the use of concepts from transfer learning to considerably reduce the tra…
Coconut Palm Tree Counting on Drone Images with Deep Object Detection and Synthetic Training Data
Tobias Rohe, Barbara Böhm, Michael Kölle +3
Drones have revolutionized various domains, including agriculture. Recent advances in deep learning have propelled among other things object detection in computer vision. This stud…
Solving Max-3SAT Using QUBO Approximation
Sebastian Zielinski, Jonas Nüßlein, Michael Kölle +3
As contemporary quantum computers do not possess error correction, any calculation performed by these devices can be considered an involuntary approximation. To solve a problem on…