  Quantum computation represents a promising frontier in the domain of
high-performance computing, blending quantum information theory with practical
applications to overcome the limitations of classical computation. This study
investigates the challenges of manufacturing high-fidelity and scalable quantum
processors. Quantum gate set tomography (QGST) is a critical method for
characterizing quantum processors and understanding their operational
capabilities and limitations. This paper introduces ML4QGST as a novel approach
to QGST by integrating machine learning techniques, specifically utilizing a
transformer neural network model. Adapting the transformer model for QGST
addresses the computational complexity of modeling quantum systems. Advanced
training strategies, including data grouping and curriculum learning, are
employed to enhance model performance, demonstrating significant congruence
with ground-truth values. We benchmark this training pipeline on the
constructed learning model, to successfully perform QGST for $3$ gates on a $1$
qubit system with over-rotation error and depolarizing noise estimation with
comparable accuracy to pyGSTi. This research marks a pioneering step in
applying deep neural networks to the complex problem of quantum gate set
tomography, showcasing the potential of machine learning to tackle nonlinear
tomography challenges in quantum computing.
