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

TL;DR

This paper presents a machine learning approach using covariance matrix adaptation to reliably tune artificial Kitaev chains to Majorana modes via tunneling spectroscopy, enabling potential topological quantum computing.

Contribution

It introduces a novel loss function based on tunneling spectroscopy features and demonstrates successful tuning of multi-site Kitaev chains using this method.

Findings

Algorithm reliably finds Majorana sweet spots in simulated chains.

The method converges towards high-quality Majorana modes.

Tunneling spectroscopy enables simultaneous tuning of multiple parameters.

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 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.