  Determining Hamiltonian parameters from noisy experimental measurements is a
key task for the control of experimental quantum systems. An experimental
platform that recently emerged, and where knowledge of Hamiltonian parameters
is crucial to fine-tune the system, is that of quantum dot-based Kitaev chains.
In this work, we demonstrate an adversarial machine learning algorithm to
determine the parameters of a quantum dot-based Kitaev chain. We train a
convolutional conditional generative adversarial neural network (Conv-cGAN)
with simulated differential conductance data and use the model to predict the
parameters at which Majorana bound states are predicted to appear. In
particular, the Conv-cGAN model facilitates a rapid, numerically efficient
exploration of the phase diagram describing the transition between elastic
co-tunneling and crossed Andreev reflection regimes. We verify the theoretical
predictions of the model by applying it to experimentally measured conductance
obtained from a minimal Kitaev chain consisting of two spin-polarized quantum
dots coupled by a superconductor-semiconductor hybrid. Our model accurately
predicts, with an average success probability of $97$\%, whether the
measurement was taken in the elastic co-tunneling or crossed Andreev
reflection-dominated regime. Our work constitutes a stepping stone towards
fast, reliable parameter prediction for tuning quantum-dot systems into
distinct Hamiltonian regimes. Ultimately, our results yield a strategy to
support Kitaev chain tuning that is scalable to longer chains.
