Learning parameter curves in feedback-based quantum optimization algorithms
The research tests if classical machine learning can predict parameter sequences for Feedback-based Quantum Algorithms (FQAs), specifically for MaxCut problems, to avoid costly qubit measurements. A teacher-student model trained on graph data successfully predicts these parameter curves accurately across various problem sizes, offering a practical way to reduce sampling costs in quantum optimization.