3 citations · 4 across the 17 of their papers we have counts for
4 papers · 2 filters
Optimizing Variational Quantum Circuits Using Metaheuristic Strategies in Reinforcement Learning
Michael Kölle, Daniel Seidl, Maximilian Zorn +3
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain…
A Study on Optimization Techniques for Variational Quantum Circuits in Reinforcement Learning
Michael Kölle, Timo Witter, Tobias Rohe +3
Quantum Computing aims to streamline machine learning, making it more effective with fewer trainable parameters. This reduction of parameters can speed up the learning process and…
Qandle: Accelerating State Vector Simulation Using Gate-Matrix Caching and Circuit Splitting
Gerhard Stenzel, Sebastian Zielinski, Michael Kölle +3
To address the computational complexity associated with state-vector simulation for quantum circuits, we propose a combination of advanced techniques to accelerate circuit executio…
Towards Federated Learning on the Quantum Internet
Leo Sünkel, Michael Kölle, Tobias Rohe +1
While the majority of focus in quantum computing has so far been on monolithic quantum systems, quantum communication networks and the quantum internet in particular are increasing…