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The Quantum Alternating Operator Ansatz (QAOA) is a hybrid quantum-classical variational algorithm for approximately solving combinatorial optimization problems on Noisy Intermediate-Scale Quantum (NISQ) devices. Although it has been successfully applied to a variety of problems, there is only limited work on correlation clustering due to the difficulty of modelling the problem constraints with the ansatz. Motivated by this, we present a generalization of QAOA that is more suitable for this problem. In particular, we modify QAOA in two ways: Firstly, we use nucleus sampling for the computation of the expected cost. Secondly, we split the problem into sub-problems, solving each individually with QAOA. We call this generalization the Sub-Problem Quantum Alternating Operator Ansatz (SQAOA) and show theoretically that optimal solutions for correlation clustering instances can be obtained with certainty when the depth of the ansatz tends to infinity. Further, we show experimentally that SQAOA achieves better approximation ratios than QAOA for correlation clustering, while using only one qubit per node of the respective problem instance and reducing the runtime (of simulations).