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TT: Fachverband Tiefe Temperaturen

TT 85: Topology: Majorana Physics II

TT 85.6: Talk

Friday, March 22, 2024, 11:00–11:15, H 3005

Machine-learned tuning of artificial Kitaev chains to Majorana sweet spots — •Jacob Benestad¹, Athanasios Tsintzis², Rubén Seoane Souto³, Martin Leijnse², and Jeroen Danon¹ — ¹Center for Quantum Spintronics, Norwegian University of Science and Technology — ²Division of Solid State Physics and NanoLund, Lund University — ³Instituto de Ciencia de Materiales de Madrid, Spanish Research Council

Artificial Kitaev chains have been proposed as a platform realising so-called “poor man’s Majorana bound states” (PMMs), lacking the topological protection of genuine Majorana bound states but still retaining non-abelian properties. These PMMs are found at discrete “sweet spots” for the Hamiltonian parameters, and the challenge in an experimental setting would be to tune the system to such a sweet spot. A recent proposal for how to experimentally probe the quality of sweet spots in artificial Kitaev chains [1] opens the door for auto-tuning of such systems. We investigate the use of Machine Learning based on an evolutionary strategy to automatically tune the Hamiltonian parameters to a sweet spot that could host PMMs.

[1] Souto et al., Phys. Rev. Research 5 (2023) 043182

Keywords: Machine Learning; Majorana Bound States; Kitaev Chains