  Noisy Intermediate-Scale Quantum (NISQ) devices are restricted by their
limited number of qubits and their short decoherence times. An approach
addressing these problems is quantum circuit cutting. It decomposes the
execution of a large quantum circuit into the execution of multiple smaller
quantum circuits with additional classical postprocessing. Since these smaller
quantum circuits require fewer qubits and gates, they are more suitable for
NISQ devices. To investigate the effect of quantum circuit cutting in a quantum
algorithm targeting NISQ devices, we design two experiments using the Quantum
Approximate Optimization Algorithm (QAOA) for the Maximum Cut (MaxCut) problem
and conduct them on state-of-the-art superconducting devices. Our first
experiment studies the influence of circuit cutting on the objective function
of QAOA, and the second evaluates the quality of results obtained by the whole
algorithm with circuit cutting. The results show that circuit cutting can
reduce the effects of noise in QAOA, and therefore, the algorithm yields better
solutions on NISQ devices.
