8 papers · 1 filter
Emergent Problem-Graph Alignment in RL-Discovered Entanglement Topologies for QAOA
Tobias Rohe, Federico Harjes Ruiloba, Markus Baumann +4
In the Quantum Approximate Optimization Algorithm (QAOA), the entanglement topology, where qubit pairs are connected by two-qubit gates, is conventionally set equal to the edge set…
Detrimental Agnostic Entanglement: The Case Against Hardware-Efficient Ansätze for Combinatorial Optimization
Tobias Rohe, Markus Baumann, Federico Harjes Ruiloba +3
Variational quantum algorithms (VQAs) for combinatorial optimization routinely employ entangling gates as a default design choice, yet the role of entanglement, in its amount and s…
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…
Topology-Guided Quantum GANs for Constrained Graph Generation
Tobias Rohe, Markus Baumann, Michael Poppel +3
Quantum computing (QC) promises theoretical advantages, benefiting computational problems that would not be efficiently classically simulatable. However, much of this theoretical s…
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…
Solving graph problems using permutation-invariant quantum machine learning
Maximilian Balthasar Mansky, Tobias Rohe, Gerhard Stenzel +7
Many computational problems are unchanged under some symmetry operation. In classical machine learning, this can be reflected with the layer structure of the neural network. In qua…