18 citations · 39 across the 17 of their papers we have counts for
6 papers · 1 filter
SAT-MARL: Specification Aware Training in Multi-Agent Reinforcement Learning
Fabian Ritz, Thomy Phan, Robert Müller +8
A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they m…
Insights on Training Neural Networks for QUBO Tasks
Thomas Gabor, Sebastian Feld, Hila Safi +2
Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAO…
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
Thomas Gabor, Leo Sünkel, Fabian Ritz +5
We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them…
Approximate Approximation on a Quantum Annealer
Irmi Sax, Sebastian Feld, Sebastian Zielinski +3
Many problems of industrial interest are NP-complete, and quickly exhaust resources of computational devices with increasing input sizes. Quantum annealers (QA) are physical device…
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA
Christoph Roch, Alexander Impertro, Thomy Phan +3
Hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of n…
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier +3
Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attentio…