2 citations · 3 across the 6 of their papers we have counts for
6 papers
Reinforcement Learning for Parameterized Quantum State Preparation: A Comparative Study
Gerhard Stenzel, Isabella Debelic, Michael Kölle +4
We extend directed quantum circuit synthesis (DQCS) with reinforcement learning from purely discrete gate selection to parameterized quantum state preparation with continuous singl…
Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication
Michael Kölle, Christian Reff, Leo Sünkel +3
Emergent cooperation in classical Multi-Agent Reinforcement Learning has gained significant attention, particularly in the context of Sequential Social Dilemmas (SSDs). While class…
Quantum King-Ring Domination in Chess: A QAOA Approach
Gerhard Stenzel, Michael Kölle, Tobias Rohe +4
The Quantum Approximate Optimization Algorithm (QAOA) is extensively benchmarked on synthetic random instances such as MaxCut, TSP, and SAT problems, but these lack semantic struct…
Weight Re-Mapping for Variational Quantum Algorithms
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +5
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum m…
Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
Jonas Stein, Tobias Rohe, Francesco Nappi +5
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximatio…
Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +3
In recent years, quantum machine learning has seen a substantial increase in the use of variational quantum circuits (VQCs). VQCs are inspired by artificial neural networks, which…