# Machine learning enables completely automatic tuning of a quantum device faster than human experts

arXiv: https://arxiv.org/abs/2001.02589 · HTML: https://mlsanity.com/paper/2001.02589/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2020.

## Built on by (most cited first, 23 of 23)

- [On scientific understanding with artificial intelligence](https://mlsanity.com/paper/2204.01467/) (2022)
- [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://mlsanity.com/paper/2405.19495/) (2024)
- [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)
- [Automated extraction of capacitive coupling for quantum dot systems](https://mlsanity.com/paper/2301.08654/) (2023)
- [Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning](https://mlsanity.com/paper/2107.12975/) (2021)
- [Fully autonomous tuning of a spin qubit](https://mlsanity.com/paper/2402.03931/) (2024)
- [Bridging the reality gap in quantum devices with physics-aware machine learning](https://mlsanity.com/paper/2111.11285/) (2021)
- [Data needs and challenges for quantum dot devices automation](https://mlsanity.com/paper/2312.14322/) (2023)
- [Theoretical bounds on data requirements for the ray-based classification](https://mlsanity.com/paper/2103.09577/) (2021)
- [Autonomous Bootstrapping of Quantum Dot Devices](https://mlsanity.com/paper/2407.20061/) (2024)
- [Identifying Pauli spin blockade using deep learning](https://mlsanity.com/paper/2202.00574/) (2022)
- [Quantum Artificial Intelligence: A Brief Survey](https://mlsanity.com/paper/2408.10726/) (2024)
- [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)
- [Machine learning optimization of Majorana hybrid nanowires](https://mlsanity.com/paper/2208.02182/) (2022)
- [Topological gap protocol based machine learning optimization of Majorana hybrid wires](https://mlsanity.com/paper/2305.16230/) (2023)
- [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)
- [Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning](https://mlsanity.com/paper/2607.09422/) (2026)
- [Evaluating Verified Autonomy in Quantum Engineering](https://mlsanity.com/paper/2609.17439/) (2026)

## Abstract (arXiv)

Device variability is a bottleneck for the scalability of semiconductor quantum devices. Increasing device control comes at the cost of a large parameter space that has to be explored in order to find the optimal operating conditions. We demonstrate a statistical tuning algorithm that navigates this entire parameter space, using just a few modelling assumptions, in the search for specific electron transport features. We focused on gate-defined quantum dot devices, demonstrating fully automated tuning of two different devices to double quantum dot regimes in an up to eight-dimensional gate voltage space. We considered a parameter space defined by the maximum range of each gate voltage in these devices, demonstrating expected tuning in under 70 minutes. This performance exceeded a human benchmark, although we recognise that there is room for improvement in the performance of both humans and machines. Our approach is approximately 180 times faster than a pure random search of the parameter space, and it is readily applicable to different material systems and device architectures. With an efficient navigation of the gate voltage space we are able to give a quantitative measurement of device variability, from one device to another and after a thermal cycle of a device. This is a key demonstration of the use of machine learning techniques to explore and optimise the parameter space of quantum devices and overcome the challenge of device variability.