https://doi.org/10.1140/epjqt/s40507-026-00528-5
Research
Reinforcement learning an entangling operation on spin qubits
1
PGI-8 (Quantum Control), Forschungszentrum Jülich, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
2
Fakultät für Physik, University of Regensburg, Universitätsstraße 31, D-93051, Regensburg, Germany
3
Fakultät für Informatik und Data Science, University of Regensburg, Universitätsstraße 31, D-93040, Regensburg, Germany
a
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Received:
10
September
2025
Accepted:
19
May
2026
Published online:
28
May
2026
Abstract
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.
Key words: Quantum control / Reinforcement learning / RL / Quantum dots / Quantum computing / Machine learning
© The Author(s) 2026
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