# Learning Coulomb Diamonds in Large Quantum Dot Arrays

arXiv: https://arxiv.org/abs/2205.01443 · HTML: https://mlsanity.com/paper/2205.01443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 5 of 5)

- [Ray-based framework for state identification in quantum dot devices](https://mlsanity.com/paper/2102.11784/) (2021)
- [Deep Reinforcement Learning for Efficient Measurement of Quantum Devices](https://mlsanity.com/paper/2009.14825/) (2020)
- [Ray-based classification framework for high-dimensional data](https://mlsanity.com/paper/2010.00500/) (2020)
- [Theoretical bounds on data requirements for the ray-based classification](https://mlsanity.com/paper/2103.09577/) (2021)
- [Estimation of Convex Polytopes for Automatic Discovery of Charge State Transitions in Quantum Dot Arrays](https://mlsanity.com/paper/2108.09133/) (2021)

## Built on by (most cited first, 2 of 2)

- [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)

## 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.