Cross-Platform Autonomous Control of Minimal Kitaev Chains
4 affiliations
- 1Delft University of Technology
- 2QuTech
- 3Aalto University
- 4Eindhoven University of Technology
Abstract
Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Poor Man’s Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Poor Man’s Majorana sweet spot. The algorithm successfully converges to an immediate vicinity of a sweet spot (within ± 1.5 m V in 67.6% of attempts and within ± 4.5 m V in 80.9% of cases), typically finding a sweet spot in 45 min or less. This advancement is a stepping stone toward autonomous tuning of emergent modes in interacting systems, and toward foundational tuning machine learning models that can be deployed across a range of experimental platforms.
Related
- Andreev exceptional points in Josephson junctions formed by minimal Kitaev chains
- Renormalization group analysis for bosonization coefficients in half-odd-integer Kitaev spin chains
- One-clean-qubit spectroscopy of simulated Kitaev chains
- Evolution from Kitaev to XXZ spin chains via distortion: Application to BaCo2V2O8
- Exact Temporal Criticalities of Entanglement and Negativity in Gaussian and Non-Gaussian Dissipative Kitaev Chains
BibTeX
@article{driel2026crossplatform,
title = {{Cross-Platform Autonomous Control of Minimal Kitaev Chains}},
author = {David van Driel and Rouven Koch and Vincent P. M. Sietses and Sebastiaan L. D. ten Haaf and Chun-Xiao Liu and Francesco Zatelli and Bart Roovers and Alberto Bordin and Nick van Loo and Guanzhong Wang and Jan Cornelis Wolff and Grzegorz P. Mazur and Tom Dvir and Ivan Kulesh and Qingzhen Wang and A. Mert Bozkurt and Saša Gazibegović and Ghada Badawy and Erik P. A. M. Bakkers and Michael Wimmer and Srijit Goswami and José Luis Lado and Leo P. Kouwenhoven and Eliška Greplová},
journal = {PRX Intelligence},
year = {2026},
month = jul,
doi = {10.1103/ryll-qb42},
url = {https://doi.org/10.1103/ryll-qb42},
note = {CC BY},
}
RIS
TY - JOUR
TI - Cross-Platform Autonomous Control of Minimal Kitaev Chains
AU - David van Driel
AU - Rouven Koch
AU - Vincent P. M. Sietses
AU - Sebastiaan L. D. ten Haaf
AU - Chun-Xiao Liu
AU - Francesco Zatelli
AU - Bart Roovers
AU - Alberto Bordin
AU - Nick van Loo
AU - Guanzhong Wang
AU - Jan Cornelis Wolff
AU - Grzegorz P. Mazur
AU - Tom Dvir
AU - Ivan Kulesh
AU - Qingzhen Wang
AU - A. Mert Bozkurt
AU - Saša Gazibegović
AU - Ghada Badawy
AU - Erik P. A. M. Bakkers
AU - Michael Wimmer
AU - Srijit Goswami
AU - José Luis Lado
AU - Leo P. Kouwenhoven
AU - Eliška Greplová
PY - 2026
DA - 2026/07/28
JO - PRX Intelligence
DO - 10.1103/ryll-qb42
UR - https://doi.org/10.1103/ryll-qb42
AB - Contemporary quantum devices are reaching new limits in size and complexity, allowing for the experimental exploration of emergent quantum modes. However, this increased complexity introduces significant challenges in device tuning and control. Here, we demonstrate autonomous tuning of emergent Poor Man’s Majorana zero modes in a minimal realization of a Kitaev chain. We achieve this task using cross-platform transfer learning. First, we train a tuning model on a theory model. Next, we retrain it using a Kitaev chain realization in a two-dimensional electron gas. Finally, we apply this model to tune a Kitaev chain realized in quantum dots coupled through a semiconductor-superconductor section in a one-dimensional nanowire. Utilizing a convolutional neural network, we predict the tunneling and Cooper pair splitting rates from differential conductance measurements, employing these predictions to adjust the electrochemical potential to a Poor Man’s Majorana sweet spot. The algorithm successfully converges to an immediate vicinity of a sweet spot (within ± 1.5 m V in 67.6% of attempts and within ± 4.5 m V in 80.9% of cases), typically finding a sweet spot in 45 min or less. This advancement is a stepping stone toward autonomous tuning of emergent modes in interacting systems, and toward foundational tuning machine learning models that can be deployed across a range of experimental platforms.
SN - 3070-0329
ER -