# Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware

arXiv: https://arxiv.org/abs/2407.17325 · HTML: https://mlsanity.com/paper/2407.17325/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2024.

## Built on by (most cited first, 8 of 8)

- [CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data](https://mlsanity.com/paper/2409.08584/) (2024)
- [Quantum-Trained Convolutional Neural Network for Deepfake Audio Detection](https://mlsanity.com/paper/2410.09250/) (2024)
- [Consensus-based Distributed Quantum Kernel Learning for Speech Recognition](https://mlsanity.com/paper/2409.05770/) (2024)
- [Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting](https://mlsanity.com/paper/2505.09395/) (2025)
- [Learning to Learn with Quantum Optimization via Quantum Neural Networks](https://mlsanity.com/paper/2505.00561/) (2025)
- [Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles](https://mlsanity.com/paper/2505.08782/) (2025)
- [Meta-Learning for Quantum Optimization via Quantum Sequence Model](https://mlsanity.com/paper/2512.05058/) (2025)
- [Special-Unitary Parameterization for Trainable Variational Quantum Circuits](https://mlsanity.com/paper/2507.05535/) (2025)

## Abstract (arXiv)

This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. Leveraging a distributed framework, we address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates. Our approach decomposes large QAOA problems into smaller subproblems, distributing them across multiple Quantum Processing Units (QPUs) to enhance scalability and performance. The noise-aware strategy incorporates error mitigation techniques to optimize qubit fidelity and gate operations, ensuring reliable quantum computations. We evaluate the efficacy of our framework using the HamilToniQ Benchmarking Toolkit, which quantifies the performance across various quantum hardware configurations. The results demonstrate that our distributed QAOA framework achieves significant improvements in computational speed and accuracy, showcasing its potential to solve complex optimization problems efficiently in the NISQ era. This work sets the stage for advanced algorithmic strategies and practical quantum system enhancements, contributing to the broader goal of achieving quantum advantage.