Learning Coulomb Diamonds in Large Quantum Dot Arrays
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Abstract (arXiv)
We introduce an algorithm that is able to find the facets of Coulomb diamonds in quantum dot arrays. We simulate these arrays using the constant-interaction model, and rely only on one-dimensional raster scans (rays) to learn a model of the device using regularized maximum likelihood estimation. This allows us to determine, for a given charge state of the device, which transitions exist and what the compensated gate voltages for these are. For smaller devices the simulator can also be used to compute the exact boundaries of the Coulomb diamonds, which we use to assess that our algorithm correctly finds the vast majority of transitions with high precision.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 3 May 2022.
Builds on
5 of 25 references.
References · 5
- 2021Ray-based framework for state identification in quantum dot devicescited by 17
- 2020Deep Reinforcement Learning for Efficient Measurement of Quantum Devicescited by 15
- 2020Ray-based classification framework for high-dimensional datacited by 10
- 2021Theoretical bounds on data requirements for the ray-based classificationcited by 4
- 2021Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrayscited by 3
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Citing papers per year: 2022: 1; 2023: 1.
2 papers.