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

TL;DR

This paper presents a noise-aware distributed QAOA framework designed for near-term quantum hardware, improving scalability, speed, and accuracy of quantum optimization on NISQ devices.

Contribution

It introduces a novel distributed, noise-aware QAOA approach that decomposes large problems and incorporates error mitigation for better performance on NISQ hardware.

Findings

Significant improvements in speed and accuracy with the distributed approach

Effective error mitigation enhances qubit fidelity and gate operations

Framework demonstrates potential for solving complex optimization problems efficiently

Abstract

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.