Quantum Approximate Optimization Algorithm: Performance on Simulators and Quantum Hardware

• URL: http://arxiv.org/abs/2509.24213v2
• Date: Wed, 08 Oct 2025 03:04:21 GMT
• Title: Quantum Approximate Optimization Algorithm: Performance on Simulators and Quantum Hardware
• Authors: Abyan Khabir Irfan, Chansu Yu,
• Abstract summary: Running quantum circuits on quantum computers does not always generate "clean" results, unlike on a simulator.<n>This paper explores different optimization methods, techniques, and error mitigation methods to observe how they react to quantum noise differently.<n>It is helpful for other researchers to understand the complexities of running QAOA on real quantum hardware and the challenges faced in dealing with noise.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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, and error mitigation methods 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.

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