论文标题
跨熵高参数优化了限制性问题的哈密顿人应用于QAOA
Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA
论文作者
论文摘要
混合量子古典算法（例如量子近似优化算法（QAOA））被认为是利用近期量子计算机在实际应用中利用近期量子计算机的最令人鼓舞的方法之一。这种算法通常以各种形式实现，将经典优化方法与量子计算机相结合，以找到优化问题的良好解决方案。 QAOA的解决方案质量在很大程度上取决于每次迭代时经典优化器选择的参数。但是，这些参数的解决方案景观是高度多维的，并且包含许多低质量的局部Optima。在这项研究中，我们采用跨凝结方法来塑造这种景观，从而使经典优化器更容易找到更好的参数，从而导致性能提高。我们从经验上证明，这种方法可以达到背包问题的明显更好的解决方案质量。
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.
