# Quantum device fine-tuning using unsupervised embedding learning

arXiv: https://arxiv.org/abs/2001.04409 · HTML: https://mlsanity.com/paper/2001.04409/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [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)
- [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)
- [Theoretical bounds on data requirements for the ray-based classification](https://mlsanity.com/paper/2103.09577/) (2021)
- [Identifying Pauli spin blockade using deep learning](https://mlsanity.com/paper/2202.00574/) (2022)
- [Confident Sinkhorn Allocation for Pseudo-Labeling](https://mlsanity.com/paper/2206.05880/) (2022)
- [QDFlow: A Python package for physics simulations of quantum dot devices](https://mlsanity.com/paper/2509.13298/) (2025)
- [Machine learning optimization of Majorana hybrid nanowires](https://mlsanity.com/paper/2208.02182/) (2022)
- [The Automated Bias Triangle Feature Extraction Framework](https://mlsanity.com/paper/2312.03110/) (2023)
- [Topological gap protocol based machine learning optimization of Majorana hybrid wires](https://mlsanity.com/paper/2305.16230/) (2023)
- [Variational Optimization for Quantum Problems using Deep Generative Networks](https://mlsanity.com/paper/2404.18041/) (2024)
- [Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning](https://mlsanity.com/paper/2607.09422/) (2026)

## Abstract (arXiv)

Quantum devices with a large number of gate electrodes allow for precise control of device parameters. This capability is hard to fully exploit due to the complex dependence of these parameters on applied gate voltages. We experimentally demonstrate an algorithm capable of fine-tuning several device parameters at once. The algorithm acquires a measurement and assigns it a score using a variational auto-encoder. Gate voltage settings are set to optimise this score in real-time in an unsupervised fashion. We report fine-tuning times of a double quantum dot device within approximately 40 min.