6 citations · 15 across the 25 of their papers we have counts for
4 papers · 1 filter
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…