# Fully autonomous tuning of a spin qubit

arXiv: https://arxiv.org/abs/2402.03931 · HTML: https://mlsanity.com/paper/2402.03931/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 74 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Deep Reinforcement Learning for Efficient Measurement of Quantum Devices](https://mlsanity.com/paper/2009.14825/) (2020)
- [Quantum device fine-tuning using unsupervised embedding learning](https://mlsanity.com/paper/2001.04409/) (2020)
- [Automated extraction of capacitive coupling for quantum dot systems](https://mlsanity.com/paper/2301.08654/) (2023)
- [Efficiently measuring a quantum device using machine learning](https://mlsanity.com/paper/1810.10042/) (2018)
- [Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning](https://mlsanity.com/paper/2107.12975/) (2021)
- [Bridging the reality gap in quantum devices with physics-aware machine learning](https://mlsanity.com/paper/2111.11285/) (2021)
- [Identifying Pauli spin blockade using deep learning](https://mlsanity.com/paper/2202.00574/) (2022)
- [The Automated Bias Triangle Feature Extraction Framework](https://mlsanity.com/paper/2312.03110/) (2023)

## Built on by (most cited first, 7 of 7)

- [Quantum computing and artificial intelligence: status and perspectives](https://mlsanity.com/paper/2505.23860/) (2025)
- [Meta-learning characteristics and dynamics of quantum systems](https://mlsanity.com/paper/2503.10492/) (2025)
- [End-to-End Analysis of Charge Stability Diagrams with Transformers](https://mlsanity.com/paper/2508.15710/) (2025)
- [Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram](https://mlsanity.com/paper/2604.13662/) (2026)
- [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)

Spanning over two decades, the study of qubits in semiconductors for quantum computing has yielded significant breakthroughs. However, the development of large-scale semiconductor quantum circuits is still limited by challenges in efficiently tuning and operating these circuits. Identifying optimal operating conditions for these qubits is complex, involving the exploration of vast parameter spaces. This presents a real 'needle in the haystack' problem, which, until now, has resisted complete automation due to device variability and fabrication imperfections. In this study, we present the first fully autonomous tuning of a semiconductor qubit, from a grounded device to Rabi oscillations, a clear indication of successful qubit operation. We demonstrate this automation, achieved without human intervention, in a Ge/Si core/shell nanowire device. Our approach integrates deep learning, Bayesian optimization, and computer vision techniques. We expect this automation algorithm to apply to a wide range of semiconductor qubit devices, allowing for statistical studies of qubit quality metrics. As a demonstration of the potential of full automation, we characterise how the Rabi frequency and g-factor depend on barrier gate voltages for one of the qubits found by the algorithm. Twenty years after the initial demonstrations of spin qubit operation, this significant advancement is poised to finally catalyze the operation of large, previously unexplored quantum circuits.