Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908847869788160 |
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| author | Benestad, Jacob Tsintzis, Athanasios Souto, Rubén Seoane Leijnse, Martin van Nieuwenburg, Evert Danon, Jeroen |
| author_facet | Benestad, Jacob Tsintzis, Athanasios Souto, Rubén Seoane Leijnse, Martin van Nieuwenburg, Evert Danon, Jeroen |
| contents | 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 tunnelling-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 neighbouring 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 tunnelling 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01240 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements Benestad, Jacob Tsintzis, Athanasios Souto, Rubén Seoane Leijnse, Martin van Nieuwenburg, Evert Danon, Jeroen Mesoscale and Nanoscale Physics 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 tunnelling-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 neighbouring 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 tunnelling 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. |
| title | Machine-learned tuning of artificial Kitaev chains from tunneling-spectroscopy measurements |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2405.01240 |