# Automated extraction of capacitive coupling for quantum dot systems

arXiv: https://arxiv.org/abs/2301.08654 · HTML: https://mlsanity.com/paper/2301.08654/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 48 references that are in the map.
- First posted on arXiv 20 Jan 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [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)
- [Ray-based classification framework for high-dimensional data](https://mlsanity.com/paper/2010.00500/) (2020)

## Built on by (most cited first, 11 of 11)

- [Fully autonomous tuning of a spin qubit](https://mlsanity.com/paper/2402.03931/) (2024)
- [Autonomous Bootstrapping of Quantum Dot Devices](https://mlsanity.com/paper/2407.20061/) (2024)
- [End-to-End Analysis of Charge Stability Diagrams with Transformers](https://mlsanity.com/paper/2508.15710/) (2025)
- [QDFlow: A Python package for physics simulations of quantum dot devices](https://mlsanity.com/paper/2509.13298/) (2025)
- [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)
- [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)
- [Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications](https://mlsanity.com/paper/2607.02413/) (2026)

## Abstract (arXiv)

Gate-defined quantum dots (QDs) have appealing attributes as a quantum computing platform. However, near-term devices possess a range of possible imperfections that need to be accounted for during the tuning and operation of QD devices. One such problem is the capacitive cross-talk between the metallic gates that define and control QD qubits. A way to compensate for the capacitive cross-talk and enable targeted control of specific QDs independent of coupling is by the use of virtual gates. Here, we demonstrate a reliable automated capacitive coupling identification method that combines machine learning with traditional fitting to take advantage of the desirable properties of each. We also show how the cross-capacitance measurement may be used for the identification of spurious QDs sometimes formed during tuning experimental devices. Our systems can autonomously flag devices with spurious dots near the operating regime, which is crucial information for reliable tuning to a regime suitable for qubit operations.