3 papers
quant-ph2026
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…
quant-ph2025
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…
quant-ph2025
From Classical Data to Quantum Advantage -- Quantum Policy Evaluation on Quantum Hardware
Daniel Hein, Simon Wiedemann, Markus Baumann +7
Quantum policy evaluation (QPE) is a reinforcement learning (RL) algorithm which is quadratically more efficient than an analogous classical Monte Carlo estimation. It makes use of…