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The Quantum Approximate Optimization Algorithm (QAOA) is a variationalquantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ)providing approximate solutions for combinatorial optimisation problems. TheQAOA utilizes a quantum-classical loop, consisting of a quantum ansatz and aclassical optimizer, to minimize some cost function computed on the quantumdevice. This paper presents an investigation into the impact of realistic noiseon the classical optimizer and the determination of optimal circuit depth forthe Quantum Approximate Optimization Algorithm (QAOA) in the presence of noise.We find that, while there is no significant difference in the performance ofclassical optimizers in a state vector simulation, the Adam and AMSGradoptimizers perform best in the presence of shot noise. Under the conditions ofreal noise, the SPSA optimizer, along with ADAM and AMSGrad, emerge as the topperformers. The study also reveals that the quality of solutions to some 5qubit minimum vertex cover problems increases for up to around six layers inthe QAOA circuit, after which it begins to decline. This analysis shows thatincreasing the number of layers in the QAOA in an attempt to increase accuracymay not work well in a noisy device.

QAOA Performance in Noisy Devices: The Effect of Classical Optimizers and Ansatz Depth