# The Automated Bias Triangle Feature Extraction Framework

arXiv: https://arxiv.org/abs/2312.03110 · HTML: https://mlsanity.com/paper/2312.03110/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 3 of 3)

- [Colloquium: Advances in automation of quantum dot devices control](https://mlsanity.com/paper/2112.09362/) (2021)
- [Quantum device fine-tuning using unsupervised embedding learning](https://mlsanity.com/paper/2001.04409/) (2020)
- [Automated extraction of capacitive coupling for quantum dot systems](https://mlsanity.com/paper/2301.08654/) (2023)

## Built on by (most cited first, 1 of 1)

- [Fully autonomous tuning of a spin qubit](https://mlsanity.com/paper/2402.03931/) (2024)

## Abstract (arXiv)

Bias triangles represent features in stability diagrams of Quantum Dot (QD) devices, whose occurrence and property analysis are crucial indicators for spin physics. Nevertheless, challenges associated with quality and availability of data as well as the subtlety of physical phenomena of interest have hindered an automatic and bespoke analysis framework, often still relying (in part) on human labelling and verification. We introduce a feature extraction framework for bias triangles, built from unsupervised, segmentation-based computer vision methods, which facilitates the direct identification and quantification of physical properties of the former. Thereby, the need for human input or large training datasets to inform supervised learning approaches is circumvented, while additionally enabling the automation of pixelwise shape and feature labeling. In particular, we demonstrate that Pauli Spin Blockade (PSB) detection can be conducted effectively, efficiently and without any training data as a direct result of this approach.