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Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements

Benestad, Jacob ; Tsintzis, Athanasios LU ; Souto, Rubén Seoane LU orcid ; Leijnse, Martin LU ; Van Nieuwenburg, Evert and Danon, Jeroen (2024) In Physical Review B 110(7).
Abstract

We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighboring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite... (More)

We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighboring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite Zeeman splitting. In both cases, the algorithm consistently converges towards high-quality sweet spots. Since tunneling spectroscopy provides one global metric for tuning all on-site potentials simultaneously, this presents a promising way towards tuning longer Kitaev chains, which are required for achieving topological protection of the Majorana modes.

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author
; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Physical Review B
volume
110
issue
7
article number
075402
publisher
American Physical Society
external identifiers
  • scopus:85200749300
ISSN
2469-9950
DOI
10.1103/PhysRevB.110.075402
language
English
LU publication?
yes
id
5916f286-737a-4c06-8dc8-6672fd2b9f20
date added to LUP
2024-09-09 16:19:33
date last changed
2025-10-14 12:29:37
@article{5916f286-737a-4c06-8dc8-6672fd2b9f20,
  abstract     = {{<p>We demonstrate reliable machine-learned tuning of quantum-dot-based artificial Kitaev chains to Majorana sweet spots, using the covariance matrix adaptation algorithm. We show that a loss function based on local tunneling spectroscopy features of a chain with two additional sensor dots added at its ends provides a reliable metric to navigate parameter space and find points where crossed Andreev reflection and elastic cotunneling between neighboring sites balance in such a way to yield near-zero-energy modes with very high Majorana quality. We simulate tuning of two- and three-site Kitaev chains, where the loss function is found from calculating the low-energy spectrum of a model Hamiltonian that includes Coulomb interactions and finite Zeeman splitting. In both cases, the algorithm consistently converges towards high-quality sweet spots. Since tunneling spectroscopy provides one global metric for tuning all on-site potentials simultaneously, this presents a promising way towards tuning longer Kitaev chains, which are required for achieving topological protection of the Majorana modes.</p>}},
  author       = {{Benestad, Jacob and Tsintzis, Athanasios and Souto, Rubén Seoane and Leijnse, Martin and Van Nieuwenburg, Evert and Danon, Jeroen}},
  issn         = {{2469-9950}},
  language     = {{eng}},
  number       = {{7}},
  publisher    = {{American Physical Society}},
  series       = {{Physical Review B}},
  title        = {{Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements}},
  url          = {{http://dx.doi.org/10.1103/PhysRevB.110.075402}},
  doi          = {{10.1103/PhysRevB.110.075402}},
  volume       = {{110}},
  year         = {{2024}},
}