# Colloquium: Advances in automation of quantum dot devices control

arXiv: https://arxiv.org/abs/2112.09362 · HTML: https://mlsanity.com/paper/2112.09362/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 109 references that are in the map.
- First posted on arXiv 17 Dec 2021.

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

- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [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)
- [Quantum device fine-tuning using unsupervised embedding learning](https://mlsanity.com/paper/2001.04409/) (2020)
- [Toward Robust Autotuning of Noisy Quantum Dot Devices](https://mlsanity.com/paper/2108.00043/) (2021)
- [Ray-based classification framework for high-dimensional data](https://mlsanity.com/paper/2010.00500/) (2020)
- [Learning convex polyhedra with margin](https://mlsanity.com/paper/1805.09719/) (2018)
- [Theoretical bounds on data requirements for the ray-based classification](https://mlsanity.com/paper/2103.09577/) (2021)
- [Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays](https://mlsanity.com/paper/2108.09133/) (2021)

## Built on by (most cited first, 18 of 18)

- [Tuning arrays with rays: Physics-informed tuning of quantum dot charge states](https://mlsanity.com/paper/2209.03837/) (2022)
- [Automated extraction of capacitive coupling for quantum dot systems](https://mlsanity.com/paper/2301.08654/) (2023)
- [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)
- [Autonomous Bootstrapping of Quantum Dot Devices](https://mlsanity.com/paper/2407.20061/) (2024)
- [Extending Explainable Boosting Machines to Scientific Image Data](https://mlsanity.com/paper/2305.16526/) (2023)
- [Automation of Quantum Dot Measurement Analysis via Explainable Machine Learning](https://mlsanity.com/paper/2402.13699/) (2024)
- [Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures](https://mlsanity.com/paper/2601.00067/) (2025)
- [The Automated Bias Triangle Feature Extraction Framework](https://mlsanity.com/paper/2312.03110/) (2023)
- [QDA2: A principled approach to automatically annotating charge stability diagrams](https://mlsanity.com/paper/2312.11206/) (2023)
- [Benchmarking machine learning models for multi-class state recognition in double quantum dot data](https://mlsanity.com/paper/2511.22451/) (2025)
- [Robust quantum dots charge autotuning using neural network uncertainty](https://mlsanity.com/paper/2406.05175/) (2024)
- [Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays](https://mlsanity.com/paper/2411.12516/) (2024)
- [Bootstrapping, autonomous testing, and initialization system for Si/SixGe1-x multi-quantum-dot devices](https://mlsanity.com/paper/2412.07676/) (2024)
- [Towards autonomous time-calibration of large quantum-dot devices: Detection, real-time feedback, and noise spectroscopy](https://mlsanity.com/paper/2512.24894/) (2025)
- [AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes](https://mlsanity.com/paper/2601.02149/) (2026)
- [From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data](https://mlsanity.com/paper/2605.01367/) (2026)
- [Machine Learning for Charge State Characterization of Isolated Double Quantum Dots](https://mlsanity.com/paper/2607.20871/) (2026)

## Abstract (arXiv)

Arrays of quantum dots (QDs) are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. In such semiconductor quantum systems, devices now have tens of individual electrostatic and dynamical voltages that must be carefully set to localize the system into the single-electron regime and to realize good qubit operational performance. The mapping of requisite QD locations and charges to gate voltages presents a challenging classical control problem. With an increasing number of QD qubits, the relevant parameter space grows sufficiently to make heuristic control unfeasible. In recent years, there has been considerable effort to automate device control that combines script-based algorithms with machine learning (ML) techniques. In this Colloquium, a comprehensive overview of the recent progress in the automation of QD device control is presented, with a particular emphasis on silicon- and GaAs-based QDs formed in two-dimensional electron gases. Combining physics-based modeling with modern numerical optimization and ML has proven effective in yielding efficient, scalable control. Further integration of theoretical, computational, and experimental efforts with computer science and ML holds vast potential in advancing semiconductor and other platforms for quantum computing.