  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.
