Knowledge Distillation in the QAOA Setting for Advantage on Quantum Hardware
Year: 2026
Type: conference-paper
Abstract: In this work, we investigate the application of Knowledge Distillation (KD) to the Quantum Approximate Optimization Algorithm (QAOA) setting, introducing a framework to distill knowledge from a Multi-angle QAOA teacher to a standard, single-layer QAOA student. We propose three distillation schemes: count-based KD, distillation using MSE, and distillation with Huber loss for enhanced robustness. Numerical experiments on diverse graph families, including complete graphs, Erdős–Rényi graphs, complete binary trees, and random 3-regular graphs, suggest that MSE and Huber-based distillation can improve the student’s performance, while count-based KD exhibits higher variance and limited generalization. Our results indicate that KD can enable a compression of the QAOA parameter space while often maintaining competitive solution quality, providing a practical avenue for efficient deployment on Noisy Intermediate-Scale Quantum hardware with improved training stability.
Institutions: Sapienza University of Rome, Middlesex University London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7165139361