Characterizing spin-valley-blockades in the bilayer graphene system

Advancements in the transport spectroscopy of 2D-material quantum-dot platforms have sparked an interest in spin-valley qubits. In this context, the Pauli blockades observed in quantum dot structures play a pivotal role in enabling the initialization and manipulation of multi-qubit systems. Concentrating on multi-quantum dot structures within the bilayer graphene framework and guided by experimental findings, we have develop multifaceted computational models, that capture the transport characteristics as a function of gate voltages as well as the physics of the underlying material. Besides accurately simulating the occurrence of Pauli blockades, our simulations have notably unveiled two remarkable phenomena: (i) the presence of multiple resonances within a bias triangle, and (ii) the manifestation of multiple spin-valley blockades. Harnessing our model to train a machine learning algorithm, we have devised an automated approach for the real-time identification of multiple Pauli blockade regimes. Through numerical forecasts and validation against test data, we pinpoint both the locations and the number of Pauli blockades likely to arise. The comprehensive and integrated computational models developed in this study thus lay a foundation for future experiments in the field of transport spectroscopy in other 2D-material platforms for the experimental realization of spin-valley qubits.