Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA

Christoph Roch, Alexander Impertro, Thomy Phan, Thomas Gabor, Sebastian Feld, Claudia Linnhoff-Popien
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引用次数: 5

Abstract

Hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. The solution quality of QAOA depends to a high degree on the parameters chosen by the classical optimizer at each iteration. However, the solution landscape of those parameters is highly multi-dimensional and contains many low-quality local optima. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical optimizer to find better parameter more easily and hence results in an improved performance. We empirically demonstrate that this approach can reach a significant better solution quality for the Knapsack Problem.
约束问题哈密顿量的交叉熵超参数优化在QAOA中的应用
量子近似优化算法(QAOA)等混合量子经典算法被认为是利用近期量子计算机在实际应用中最令人鼓舞的方法之一。这种算法通常以变分形式实现,将经典优化方法与量子机器相结合,以找到优化问题的良好解。QAOA的解质量在很大程度上取决于经典优化器在每次迭代时所选择的参数。然而,这些参数的解格局是高度多维的,并且包含许多低质量的局部最优。在本研究中,我们应用交叉熵方法来塑造这种景观,这使得经典优化器更容易找到更好的参数,从而提高性能。我们的经验证明,这种方法可以达到一个明显更好的解质量的背包问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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