Hamiltonian-Guided Leverage Embedding: Robust Subspace Compression for Efficient QAOA Parameter Estimation
The HGLE algorithm creates a hybrid quantum-classical pipeline to estimate QAOA parameters efficiently. It takes low-energy quantum samples, encodes them into a feature matrix, and compresses this matrix using leverage-score sampling to a lower rank. This compression allows classical optimization in a reduced space, making parameter estimation more robust against noise and significantly faster.