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

arXiv: https://arxiv.org/abs/2508.15710 · HTML: https://mlsanity.com/paper/2508.15710/

## 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 (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks](https://mlsanity.com/paper/1711.06753/) (2017)
- [Transformers Can Achieve Length Generalization But Not Robustly](https://mlsanity.com/paper/2402.09371/) (2024)
- [End to end learning and optimization on graphs](https://mlsanity.com/paper/1905.13732/) (2019)
- [Transformers are Meta-Reinforcement Learners](https://mlsanity.com/paper/2206.06614/) (2022)
- [Limits of End-to-End Learning](https://mlsanity.com/paper/1704.08305/) (2017)
- [Ray-based framework for state identification in quantum dot devices](https://mlsanity.com/paper/2102.11784/) (2021)
- [Deep Reinforcement Learning for Efficient Measurement of Quantum Devices](https://mlsanity.com/paper/2009.14825/) (2020)
- [Distribution Transformers: Fast Approximate Bayesian Inference With On-The-Fly Prior Adaptation](https://mlsanity.com/paper/2502.02463/) (2025)
- [Automated extraction of capacitive coupling for quantum dot systems](https://mlsanity.com/paper/2301.08654/) (2023)
- [Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning](https://mlsanity.com/paper/2107.12975/) (2021)
- [Fully autonomous tuning of a spin qubit](https://mlsanity.com/paper/2402.03931/) (2024)
- [Bridging the reality gap in quantum devices with physics-aware machine learning](https://mlsanity.com/paper/2111.11285/) (2021)
- [Mini but Mighty: Finetuning ViTs with Mini Adapters](https://mlsanity.com/paper/2311.03873/) (2023)
- [Quantum feedback control with a transformer neural network architecture](https://mlsanity.com/paper/2411.19253/) (2024)
- [Meta-learning characteristics and dynamics of quantum systems](https://mlsanity.com/paper/2503.10492/) (2025)
- [Advances in Transformers for Robotic Applications: A Review](https://mlsanity.com/paper/2412.10599/) (2024)

## Built on by (most cited first, 3 of 3)

- [Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures](https://mlsanity.com/paper/2601.00067/) (2025)
- [Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models](https://mlsanity.com/paper/2603.26432/) (2026)
- [Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning](https://mlsanity.com/paper/2607.09422/) (2026)

## 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.