[We-P] Quantum technologies: semiconductor qubits and applications, cryo-CMOS technologies

[We-P-105] Model Based Auto-Tuning of Double Quantum Dots

〇Duanlian Zhang¹, Raisei Mizokuchi¹, Shunsuke Ota¹, Riku Wada¹, Tetsuo Kodera¹ (1. Inst. of Sci. Tokyo (Japan))

We present a learning-based framework for automated double quantum dot tuning in a simplified simulation environment as a step toward scalable auto-tuning. Combining CNN-based charge-state recognition with model-based reinforcement learning, the method consistently reaches the target regime across repeated runs.