  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.
