Cross-problem parameter transferability in Quantum Approximate Optimization Algorithm

Our latest work on making Quantum Approximate Optimization Algorithm more efficient through parameter transferability. Now it is the transferability across combinatorial optimization problems. Imagine a scenario where we have pre-trained QAOA parameters on a large collection of known optimization problems during the preprocessing. But then, a new problem class emerges, perhaps less studied or previously unknown. Should we invest substantial computational resources to re-optimize from scratch? 

We propose a graph neural network-based retrieval framework that learns graph embeddings and predicts transferable QAOA parameter sets optimized for MaxCut to accelerate convergence on Maximum Independent Set. Congratulations to PhD students Kien Nguyen and Bao Bach!

Check our paper at https://arxiv.org/abs/2504.10733