  Contemporary quantum devices are reaching new limits in size and complexity,
allowing for the experimental exploration of emergent quantum modes. However,
this increased complexity introduces significant challenges in device tuning
and control. Here, we demonstrate autonomous tuning of emergent Majorana zero
modes in a minimal realization of a Kitaev chain. We achieve this task using
cross-platform transfer learning. First, we train a tuning model on a theory
model. Next, we retrain it using a Kitaev chain realization in a
two-dimensional electron gas. Finally, we apply this model to tune a Kitaev
chain realized in quantum dots coupled through a semiconductor-superconductor
section in a one-dimensional nanowire. Utilizing a convolutional neural
network, we predict the tunneling and Cooper pair splitting rates from
differential conductance measurements, employing these predictions to adjust
the electrochemical potential to a Majorana sweet spot. The algorithm
successfully converges to the immediate vicinity of a sweet spot (within 1.5 mV
in 67.6% of attempts and within 4.5 mV in 80.9% of cases), typically finding a
sweet spot in 45 minutes or less. This advancement is a stepping stone towards
autonomous tuning of emergent modes in interacting systems, and towards
foundational tuning machine learning models that can be deployed across a range
of experimental platforms.
