Cross-Platform Autonomous Control of Minimal Kitaev Chains

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 Majo...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2024-05
Main Authors: David van Driel, Koch, Rouven, Sietses, Vincent P M, Sebastiaan L D ten Haaf, Chun-Xiao, Liu, Zatelli, Francesco, Roovers, Bart, Bordin, Alberto, Nick van Loo, Wang, Guanzhong, Wolff, Jan Cornelis, Mazur, Grzegorz P, Dvir, Tom, Kulesh, Ivan, Wang, Qingzhen, A Mert Bozkurt, Gazibegovic, Sasa, Badawy, Ghada, Erik P A M Bakkers, Wimmer, Michael, Goswami, Srijit, Lado, Jose L, Kouwenhoven, Leo P, Greplova, Eliska
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Complexity
Cooper pairs
Electrochemical potential
Electron gas
Machine learning
Nanowires
Quantum dots
Tuning
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 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 Majorana sweet spot. The algorithm successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a sweet spot in 45 minutes or less. This advancement is a stepping stone towards autonomous tuning of emergent modes in interacting systems, and towards foundational tuning machine learning models that can be deployed across a range of experimental platforms.
ISSN:2331-8422