Machine-learned tuning of artificial Kitaev chains from tunneling spectroscopy measurements
(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.
(Less)
- author
- Benestad, Jacob
; Tsintzis, Athanasios
LU
; Souto, Rubén Seoane
LU
; Leijnse, Martin LU ; Van Nieuwenburg, Evert and Danon, Jeroen
- organization
- publishing date
- 2024-08
- 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
- 2024-09-09 16:19:33
@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}}, }