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Cross-Platform Autonomous Control of Minimal Kitaev Chains

David van Driel1,*, Rouven Koch1,2, Vincent P. M. Sietses1, Sebastiaan L. D. ten Haaf1, Chun-Xiao Liu1, Francesco Zatelli1, Bart Roovers1, Alberto Bordin1, Nick van Loo1 et al.

Guanzhong Wang1, Jan Cornelis Wolff1, Grzegorz P. Mazur1, Tom Dvir1, Ivan Kulesh1, Qingzhen Wang1, A. Mert Bozkurt1, Sasa Gazibegovic3, Ghada Badawy3, Erik P. A. M. Bakkers3, Michael Wimmer1, Srijit Goswami1, Jose L. Lado2, Leo P. Kouwenhoven1, and Eliska Greplova1,†

  • *Contact author: D.vanDriel@tudelft.nl
  • †Contact author: e.greplova@tudelft.nl
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PRX Intelligence 1, 013005 – Published 28 July, 2026

DOI: https://doi.org/10.1103/ryll-qb42

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 ±1.5mV in 67.6% of attempts and within ±4.5mV 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.

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PRX Intelligence 1, 013005– Published 28 July, 2026
  • Received 31 May 2024
  • Accepted 4 June 2026
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