# Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm

arXiv: https://arxiv.org/abs/2403.03310 · HTML: https://mlsanity.com/paper/2403.03310/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization](https://mlsanity.com/paper/2110.11331/) (2021)
- [Semi-Supervised Graph Imbalanced Regression](https://mlsanity.com/paper/2305.12087/) (2023)
- [Graph neural network initialisation of quantum approximate optimisation](https://mlsanity.com/paper/2111.03016/) (2021)
- [Fast Simulation of High-Depth QAOA Circuits](https://mlsanity.com/paper/2309.04841/) (2023)
- [Bridging Classical and Quantum with SDP initialized warm-starts for QAOA](https://mlsanity.com/paper/2010.14021/) (2020)
- [Qplacer: Frequency-Aware Component Placement for Superconducting Quantum Computers](https://mlsanity.com/paper/2401.17450/) (2024)

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

- [DiffQ: Unified Parameter Initialization for Variational Quantum Algorithms via Diffusion Models](https://mlsanity.com/paper/2509.17324/) (2025)

## Abstract (arXiv)

In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that have long challenged classical computational methods. Among these, the Quantum Approximate Optimization Algorithm (QAOA) stands out for its potential to efficiently solve the Max-Cut problem, a quintessential example of combinatorial optimization. However, practical application faces challenges due to current limitations on quantum computational resource. Our work optimizes QAOA initialization, using Graph Neural Networks (GNN) as a warm-start technique. This sacrifices affordable computational resource on classical computer to reduce quantum computational resource overhead, enhancing QAOA's effectiveness. Experiments with various GNN architectures demonstrate the adaptability and stability of our framework, highlighting the synergy between quantum algorithms and machine learning. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing and contributing to practical applications.