mlsanityData to 7 Oct 2026

Quantum device fine-tuning using unsupervised embedding learning

arXiv 2001.04409 ↗First posted 13 Jan 2020cond-mat.mes-hall cs.LG quant-phcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 2; 2024: 3; 2025: 1; 2026: 1.

14 papers.

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  1. 2024Qiskit Code Assistant: Training LLMs for generating Quantum Computing Codecited by 24
  2. 2021Colloquium: Advances in automation of quantum dot devices controlcited by 18
  3. 2021Ray-based framework for state identification in quantum dot devicescited by 17
  4. 2021Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learningcited by 7
  5. 2024Fully autonomous tuning of a spin qubitcited by 7
  6. 2021Theoretical bounds on data requirements for the ray-based classificationcited by 4
  7. 2022Identifying Pauli spin blockade using deep learningcited by 3
  8. 2022Confident Sinkhorn Allocation for Pseudo-Labelingcited by 3
  9. 2025QDFlow: A Python package for physics simulations of quantum dot devicescited by 2
  10. 2022Machine learning optimization of Majorana hybrid nanowirescited by 1
  11. 2023The Automated Bias Triangle Feature Extraction Frameworkcited by 1
  12. 2023Topological gap protocol based machine learning optimization of Majorana hybrid wirescited by 0
2 more
  1. 2024Variational Optimization for Quantum Problems using Deep Generative Networkscited by 0
  2. 2026Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuningcited by 0