Data needs and challenges for quantum dot devices automation
Not on the map. Why?
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.
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
8 of 56 references.
References · 8
- 2020Machine learning enables completely automatic tuning of a quantum device faster than human expertscited by 23
- 2021Colloquium: Advances in automation of quantum dot devices controlcited by 18
- 2021Ray-based framework for state identification in quantum dot devicescited by 17
- 2022Tuning arrays with rays: Physics-informed tuning of quantum dot charge statescited by 16
- 2021Toward Robust Autotuning of Noisy Quantum Dot Devicescited by 13
- 2017HEPData: a repository for high energy physics datacited by 6
- 2022Identifying Pauli spin blockade using deep learningcited by 3
- 2023QDA2: A principled approach to automatically annotating charge stability diagramscited by 1
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2025Quantum computing and artificial intelligence: status and perspectivescited by 6
- 2025QDFlow: A Python package for physics simulations of quantum dot devicescited by 2
- 2024Bootstrapping, autonomous testing, and initialization system for Si/SixGe1-x multi-quantum-dot devicescited by 0
- 2026Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Modelscited by 0
- 2026Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuningcited by 0
- 2026Machine Learning for Charge State Characterization of Isolated Double Quantum Dotscited by 0
- 2026Machine Learning for Charge State Characterization of Isolated Double Quantum Dots23 Jul 2026
- 2026Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning10 Jul 2026
- 2026Reconstructing Quantum Dot Charge Stability Diagrams with Diffusion Models27 Mar 2026
- 2025QDFlow: A Python package for physics simulations of quantum dot devices16 Sep 2025
- 2025Quantum computing and artificial intelligence: status and perspectives29 May 2025
- 2024Bootstrapping, autonomous testing, and initialization system for Si/SixGe1-x multi-quantum-dot devices10 Dec 2024