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Determining Hamiltonian parameters from noisy experimental measurements is a key task for the control of experimental quantum systems. An interesting experimental platform where precise knowledge of device parameters is useful is the quantum-dot-based Kitaev chain. In these systems, the fine tuning of Hamiltonian parameters is crucial in order to reach the desired regime with stable midgap modes. 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 (CCGAN) 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 CCGAN 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., QRD/Kouwenhoven Lab, QN/Greplová Lab

Quasi-flat-bands emerging in buckled monolayer graphene superlattices have been recently shown to realize correlated states analogous to those observed in twisted graphene multilayers. Here, we demonstrate the emergence of valley topology driven by competing electronic correlations in buckled graphene superlattices. We show, both by means of atomistic models and a low-energy description, that the existence of long-range electronic correlations leads to a competition between antiferromagnetic and charge density wave instabilities, that can be controlled by means of screening engineering. Interestingly, we find that the emergent charge density wave has a topologically non-trivial electronic structure, leading to a coexistent quantum valley Hall insulating state. In a similar fashion, the antiferromagnetic phase realizes a spin-polarized quantum valley-Hall insulating state. Our results put forward buckled graphene superlattices as a new platform to realize interaction-induced topological matter., Accepted Author Manuscript, QN/Akhmerov Group

Quasi-flat-bands emerging in buckled monolayer graphene superlattices have been recently shown to realize correlated states analogous to those observed in twisted graphene multilayers. Here, we demonstrate the emergence of valley topology driven by competing electronic correlations in buckled graphene superlattices. We show, both by means of atomistic models and a low-energy description, that the existence of long-range electronic correlations leads to a competition between antiferromagnetic and charge density wave instabilities, that can be controlled by means of screening engineering. Interestingly, we find that the emergent charge density wave has a topologically non-trivial electronic structure, leading to a coexistent quantum valley Hall insulating state. In a similar fashion, the antiferromagnetic phase realizes a spin-polarized quantum valley-Hall insulating state. Our results put forward buckled graphene superlattices as a new platform to realize interaction-induced topological matter., Accepted Author Manuscript, QN/Akhmerov Group