Dresden 2026 – wissenschaftliches Programm

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QI: Fachverband Quanteninformation

QI 6: Implementations II

QI 6.7: Vortrag

Dienstag, 10. März 2026, 11:45–12:00, BEY/0245

Reinforcement learning entangling operations on spin qubits — •Mohammad Abedi^{1, 2} and Markus Schmitt^{1, 3} — ¹PGI-8 (Quantum Control), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428, Jülich — ²Fakultät für Physik, Universität Regensburg, Universitätsstraße 31, D-93051, Regensburg — ³Fakultät für Informatik und Data Science, Universität Regensburg, Universitätsstraße 31, D-93040, Regensburg

High-fidelity control of one- and two-qubit gates past the error correction threshold is an essential ingredient for scalable quantum computing. We present a reinforcement learning (RL) approach to find entangling protocols for semiconductor-based singlet-triplet qubits in a double quantum dot. Despite the presence of realistically modelled experimental constraints, such as various noise contributions and finite rise-time effects, we demonstrate that an RL agent can yield performative protocols, while avoiding the model-biases of traditional gradient-based methods. We optimise our RL approach for different regimes and tasks, including training from simulated process tomography reconstruction of unitary gates, and investigate the nuances of RL agent design.

Keywords: quantum control; reinforcement learning; quantum dots; quantum computing; machine learning