mlsanityData to 14 Oct 2026

End-to-End Analysis of Charge Stability Diagrams with Transformers

arXiv 2508.15710 ↗First posted 21 Aug 2025cond-mat.mes-hall cond-mat.mtrl-sci cs.LGcited by 3

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Abstract (arXiv)

Transformer models and end-to-end learning frameworks are rapidly revolutionizing the field of artificial intelligence. In this work, we apply object detection transformers to analyze charge stability diagrams in semiconductor quantum dot arrays, a key task for achieving scalability with spin-based quantum computing. Specifically, our model identifies triple points and their connectivity, which is crucial for virtual gate calibration, charge state initialization, drift correction, and pulse sequencing. We show that it surpasses convolutional neural networks in performance on three different spin qubit architectures, all without the need for retraining. In contrast to existing approaches, our method significantly reduces complexity and runtime, while enhancing generalizability. The results highlight the potential of transformer-based end-to-end learning frameworks as a foundation for a scalable, device- and architecture-agnostic tool for control and tuning of quantum dot devices.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 21 Aug 2025.

Builds on

24 of 57 references.

References · 24

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12 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructurescited by 2
  2. 2026Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Modelscited by 0
  3. 2026Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuningcited by 0