Statistical Learning for Initialization of Quantum Dot Arrays

In the race toward the realization of a scalable quantum computer, spin qubits in quantum dots are amongst the most promising candidates due to their high logic-gate precision and direct compatibility with industrial fabrication techniques. However, initializing quantum dots in the qubit regime remains a daunting task and impairs the development of multiqubit prototypes. In this interdisciplinary project we address this issue by seeking the realization of a statistical learning algorithm inspired by artificial intelligence techniques to design new automated initialization protocols of quantum dot arrays. To drive research focus toward more complex devices, the final version of the algorithm will aim for sufficient versatility to comply with a wide variety of quantum dot architectures.