# Identifying Pauli spin blockade using deep learning

arXiv: https://arxiv.org/abs/2202.00574 · HTML: https://mlsanity.com/paper/2202.00574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 1 Feb 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Machine learning enables completely automatic tuning of a quantum device faster than human experts](https://mlsanity.com/paper/2001.02589/) (2020)
- [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)
- [Quantum device fine-tuning using unsupervised embedding learning](https://mlsanity.com/paper/2001.04409/) (2020)
- [Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning](https://mlsanity.com/paper/2107.12975/) (2021)
- [Bridging the reality gap in quantum devices with physics-aware machine learning](https://mlsanity.com/paper/2111.11285/) (2021)

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

- [Fully autonomous tuning of a spin qubit](https://mlsanity.com/paper/2402.03931/) (2024)
- [Data needs and challenges for quantum dot devices automation](https://mlsanity.com/paper/2312.14322/) (2023)
- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)

## Abstract (arXiv)

Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.