Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overall performance is dependent on the appearance of local minima and barren plateaus, which slow-down calculations and lead to non-optimal solutions. In practice, this results in dramatic computational and energy costs for AI applications. In this paper we introduce a generic strategy to accelerate and improve the overall performance of such methods, allowing to alleviate the effect of barren plateaus and local minima. Our method is based on coordinate transformations, somehow similar to variational rotations, adding extra directions in parameter space that depend on the cost function itself, and which allow to explore the configuration landscape more efficiently. The validity of our method is benchmarked by boosting a number of quantum machine learning algorithms, getting a very significant improvement in their performance.

翻译：机器学习算法，无论是经典版本还是量子版本，都严重依赖于基于梯度的优化算法，例如梯度下降及其变体。整体性能取决于局部极小值和贫瘠高原的出现，这些现象会减慢计算速度并导致非最优解。在实践中，这会导致人工智能应用产生巨大的计算和能源成本。本文提出了一种通用策略，用于加速和改进此类方法的整体性能，从而减轻贫瘠高原和局部极小值的负面影响。我们的方法基于坐标变换，与变分旋转有些类似，在参数空间中添加了依赖于代价函数本身的额外方向，从而能够更高效地探索构型空间。通过提升多个量子机器学习算法的性能来验证我们方法的有效性，并观察到其性能得到了非常显著的提升。