Machine Learning for Charge State Characterization of Isolated Double Quantum Dots
Scaling semiconductor quantum dot arrays toward fault-tolerant quantum computing requires efficient tuneup of spin qubits, a process that depends on the analysis of charge stability maps (CSMs) and remains largely manual. While machine learning has been widely applied to CSM analysis in reservoir-coupled devices, automated tuning in the increasingly important isolated-mode regime has received limited attention. In isolated-mode CSMs, charge transitions appear as near-vertical lines, making them well suited to compact, task-specific models. We present two convolutional neural networks with fewer than one million parameters, trained on CSMs collected from 32 silicon metal-oxide-semiconducto...
| Authors | Hyma Vallabhapurapu, Marco Candido, Krishna Choudhary et al. |
|---|---|
| Primary category | cond-mat.mes-hall |
| Published | 23 July 2026 |
| Last updated at source | 23 July 2026 |
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