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Federico Harjes Ruiloba

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#reinforcement learning Open access Aug 2026

Emergent problem-graph alignment in RL-discovered entanglement topologies for QAOA

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 of the problem graph. This coupling ties circuit design to explicit problem knowledge and may not yield the most trainable circuit under li...

Tobias Rohe, Federico Harjes Ruiloba, Markus Baumann et al. · Frontiers in Quantum Science... · 0 citations

Preprint Aug 2026

Implicit Differentiation for Measurement-Efficient Bilevel Quantum-Classical Optimization

This work proposes a bilevel optimization model for diagonal cost Hamiltonians where coefficients depend on a tunable outer parameter and develops correlator-reuse implicit differentiation (CR-ID), which obtains outer gradients by reusing quantum measurements already collected during inner energy estimation, requiring...

Tobias Rohe, M. Baumann, Federico Harjes Ruiloba et al. · 0 citations