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Hybrid quantum-classical algorithms such as the Quantum ApproximateOptimization Algorithm (QAOA) are considered as one of the most encouragingapproaches for taking advantage of near-term quantum computers in practicalapplications. Such algorithms are usually implemented in a variational form,combining a classical optimization method with a quantum machine to find goodsolutions to an optimization problem. The solution quality of QAOA depends to ahigh degree on the parameters chosen by the classical optimizer at eachiteration. However, the solution landscape of those parameters is highlymulti-dimensional and contains many low-quality local optima. In this study weapply a Cross-Entropy method to shape this landscape, which allows theclassical optimizer to find better parameter more easily and hence results inan improved performance. We empirically demonstrate that this approach canreach a significant better solution quality for the Knapsack Problem.

Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA