Reinforcement learning entangling operations on spin qubits

• URL: http://arxiv.org/abs/2508.14761v1
• Date: Wed, 20 Aug 2025 15:05:38 GMT
• Title: Reinforcement learning entangling operations on spin qubits
• Authors: Mohammad Abedi, Markus Schmitt,
• Abstract summary: We present a reinforcement learning approach to find entangling protocols for semiconductor-based singlet-triplet qubits in a double quantum dot.<n>We demonstrate that an RL agent can yield performative protocols, while avoiding the model-biases of traditional gradient-based methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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