Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance. However, the main limitation is the constraint to linear operations that hampers the representation of complex relationships in data. In this work, we propose an efficient implementation of quantum splines for non-linear approximation. In particular, we first discuss possible parametrisations, and select the most convenient for exploiting the HHL algorithm to obtain the estimates of spline coefficients. Then, we investigate QSpline performance as an evaluation routine for some of the most popular activation functions adopted in ML. Finally, a detailed comparison with classical alternatives to the HHL is also presented.
翻译：量子计算为高效计算提供了一个新的范例,许多AI应用可受益于其潜在性能提升。然而,主要的限制是对线性操作的限制,这妨碍了数据中复杂关系的代表性。在这项工作中,我们建议高效实施非线性近似量子样条。特别是,我们首先讨论可能的相近性,并选择最方便的利用HHL算法获得样板系数估计值的方法。然后,我们调查QSpline性能,作为ML中采用的一些最流行的激活功能的评价常规。最后,还详细比较了HL的典型替代品。</s>
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