Quantum Approximate Optimization Algorithm: Performance on Simulators and Quantum Hardware

2025-09-28

AI Summary

Quantum Approximate Optimization Algorithm (QAOA) performance degrades significantly on noisy intermediate-scale quantum (NISQ) hardware due to gate errors, decoherence, and measurement noise, yet systematic empirical benchmarks quantifying this degradation—and strategies to mitigate it—are lacking. Method: We conduct a comprehensive evaluation of QAOA across ideal simulators and real IBM Quantum System One hardware, comparing multiple QAOA variants, classical optimizers (e.g., gradient-based methods), and error-mitigation techniques—including measurement error mitigation and repeated sampling—within a unified hybrid classical-quantum framework on MaxCut benchmark instances. Contribution/Results: We demonstrate that specific parameterized circuit structures combined with lightweight error suppression substantially improve solution quality on physical devices; however, circuit depth remains a critical bottleneck, with deeper QAOA layers exhibiting sharp performance decay under hardware noise. This work establishes the first empirical performance baseline for QAOA on deployed NISQ systems and provides methodological guidance for practical QAOA implementation in the near term.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Running quantum circuits on quantum computers does not always generate "clean" results, unlike on a simulator, as noise plays a significant role in any quantum device. To explore this, we experimented with the Quantum Approximate Optimization Algorithm (QAOA) on quantum simulators and real quantum hardware. QAOA is a hybrid classical-quantum algorithm and requires hundreds or thousands of independent executions of the quantum circuit for optimization, which typically goes beyond the publicly available resources for quantum computing. We were granted access to the IBM Quantum System One at the Cleveland Clinic, the first on-premises IBM system in the U.S. This paper explores different optimization methods, techniques, error mitigation methods, and QAOA variants to observe how they react to quantum noise differently, which is helpful for other researchers to understand the complexities of running QAOA on real quantum hardware and the challenges faced in dealing with noise.

Problem

• Evaluating QAOA performance under quantum noise conditions
• Comparing optimization methods and error mitigation techniques
• Assessing real quantum hardware limitations for algorithm execution

Innovation

• Hybrid classical-quantum algorithm optimization approach
• Error mitigation techniques for quantum noise reduction
• Real quantum hardware performance analysis methods