# Data needs and challenges for quantum dot devices automation

arXiv: https://arxiv.org/abs/2312.14322 · HTML: https://mlsanity.com/paper/2312.14322/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 56 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 8 of 8)

- [Machine learning enables completely automatic tuning of a quantum device faster than human experts](https://mlsanity.com/paper/2001.02589/) (2020)
- [Colloquium: Advances in automation of quantum dot devices control](https://mlsanity.com/paper/2112.09362/) (2021)
- [Ray-based framework for state identification in quantum dot devices](https://mlsanity.com/paper/2102.11784/) (2021)
- [Tuning arrays with rays: Physics-informed tuning of quantum dot charge states](https://mlsanity.com/paper/2209.03837/) (2022)
- [Toward Robust Autotuning of Noisy Quantum Dot Devices](https://mlsanity.com/paper/2108.00043/) (2021)
- [HEPData: a repository for high energy physics data](https://mlsanity.com/paper/1704.05473/) (2017)
- [Identifying Pauli spin blockade using deep learning](https://mlsanity.com/paper/2202.00574/) (2022)
- [QDA2: A principled approach to automatically annotating charge stability diagrams](https://mlsanity.com/paper/2312.11206/) (2023)

## Built on by (most cited first, 6 of 6)

- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [QDFlow: A Python package for physics simulations of quantum dot devices](https://mlsanity.com/paper/2509.13298/) (2025)
- [Bootstrapping, autonomous testing, and initialization system for Si/SixGe1-x multi-quantum-dot devices](https://mlsanity.com/paper/2412.07676/) (2024)
- [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)
- [Machine Learning for Charge State Characterization of Isolated Double Quantum Dots](https://mlsanity.com/paper/2607.20871/) (2026)

## Abstract (arXiv)

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.