pith. sign in

arxiv: 2405.04596 · v2 · pith:W4YZUJAXnew · submitted 2024-05-07 · ❄️ cond-mat.mes-hall · cond-mat.dis-nn

Cross-Platform Autonomous Control of Minimal Kitaev Chains

David van Driel , Rouven Koch , Vincent P. M. Sietses , Sebastiaan L. D. ten Haaf , Chun-Xiao Liu , Francesco Zatelli , Bart Roovers , Alberto Bordin
show 16 more authors
Nick van Loo Guanzhong Wang Jan Cornelis Wolff Grzegorz P. Mazur Tom Dvir Ivan Kulesh Qingzhen Wang A. Mert Bozkurt Sasa Gazibegovic Ghada Badawy Erik P. A. M. Bakkers Michael Wimmer Srijit Goswami Jose L. Lado Leo P. Kouwenhoven Eliska Greplova
This is my paper
classification ❄️ cond-mat.mes-hall cond-mat.dis-nn
keywords tuningkitaevautonomouschainemergentmodelmodesquantum
0
0 comments X
read the original 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 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Assessing Majorana states and qubits through quantum capacitance

    cond-mat.mes-hall 2026-06 unverdicted novelty 6.0

    Quantum capacitance with an auxiliary quantum dot measures ground-state energy splitting and Majorana overlap in topological qubits via peak positions and magnitudes in even and odd parity sectors.

  2. Hamiltonian learning for spin-spiral moir\'e magnets from electronic magnetotransport

    cond-mat.mes-hall 2026-04 unverdicted novelty 6.0

    Supervised ML trained on field- and bias-dependent conductance extracts the q-vector of arbitrary spin-spiral magnets in 2D moiré systems.

  3. AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

    cond-mat.mes-hall 2026-01 unverdicted novelty 6.0

    A vision-transformer neural network trained unsupervised on synthetic conductance data proposes Hamiltonian parameter updates that drive quantum dot chains into the topological phase with Majorana modes, often succeed...

  4. Learning Inhomogeneous Heisenberg Hamiltonians in Nanographene Spin Chains

    cond-mat.mes-hall 2026-06 unverdicted novelty 5.0

    Machine learning reconstructs inhomogeneous Heisenberg Hamiltonians from inelastic STS maps in nanographene spin chains and reproduces spectra plus gap scaling.

  5. g-tensor Optimization in Ge/SiGe Quantum Dots

    cond-mat.mes-hall 2026-04 unverdicted novelty 4.0

    A flexible optimization framework is introduced to suppress in-plane g-tensor components in SiGe-Ge-SiGe quantum wells by tuning silicon concentration, enabling gapless single-spin qubit encoding.