# QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks

arXiv: https://arxiv.org/abs/2504.21135 · HTML: https://mlsanity.com/paper/2504.21135/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [How Attentive are Graph Attention Networks?](https://mlsanity.com/paper/2105.14491/) (2021)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Challenges and Opportunities in Quantum Optimization](https://mlsanity.com/paper/2312.02279/) (2023)
- [Finding Near-Optimal Independent Sets at Scale](https://mlsanity.com/paper/1509.00764/) (2015)
- [Exactly Solving the Maximum Weight Independent Set Problem on Large Real-World Graphs](https://mlsanity.com/paper/1810.10834/) (2018)
- [QAOA-GPT: Efficient Generation of Adaptive and Regular Quantum Approximate Optimization Algorithm Circuits](https://mlsanity.com/paper/2504.16350/) (2025)
- [Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach](https://mlsanity.com/paper/2504.10733/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning to Prepare Molecular Ground States with Transformer Models](https://mlsanity.com/paper/2607.22468/) (2026)

## Abstract (arXiv)

The quantum approximate optimization algorithm (QAOA) is one of the promising variational approaches of quantum computing to solve combinatorial optimization problems. In QAOA, variational parameters need to be optimized by solving a series of nonlinear, nonconvex optimization programs. In this work, we propose a QAOA parameter transfer scheme using Graph Attention Networks (GAT) to solve Maximum Independent Set (MIS) problems. We prepare optimized parameters for graphs of 12 and 14 vertices and use GATs to transfer their parameters to larger graphs. Additionally, we design a hybrid distributed resource-aware algorithm for MIS (HyDRA-MIS), which decomposes large problems into smaller ones that can fit onto noisy intermediate-scale quantum (NISQ) computers. We integrate our GAT-based parameter transfer approach to HyDRA-MIS and demonstrate competitive results compared to KaMIS, a state-of-the-art classical MIS solver, on graphs with several thousands vertices.