Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. However, the practical advantages of using quantum resources to solve pattern recognition tasks are still to be demonstrated. This work proposes a universal, efficient framework that can reproduce the output of a plethora of classical supervised machine learning algorithms exploiting quantum computation's advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple and diverse functions to solve typical supervised learning problems. In its general formulation, MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions, such as ensemble algorithms and neural networks. From a computational point of view, the proposed framework allows generating an exponentially large number of different transformations of the input at the cost of increasing the depth of the corresponding quantum circuit linearly. Thus, MAQA produces a model with substantial descriptive power to broaden the horizon of possible applications of quantum machine learning with a computational advantage over classical methods. As a second meaningful addition, we discuss the adoption of the proposed framework as hybrid quantum-classical and fault-tolerant quantum algorithm.

翻译：量子机器学习有望改进传统机器学习方法，并克服经典计算范式所施加的一些主要局限性。然而，利用量子资源解决模式识别任务的实际优势仍有待证明。本文提出了一种通用且高效的框架，该框架能够利用量子计算的优势复现大量经典监督机器学习算法的输出。该框架名为多重聚合量子算法（MAQA），因其能够组合多种不同函数来解决典型的监督学习问题而得名。在其通用公式中，MAQA可潜在地作为所有属于多函数聚合模式（如集成算法和神经网络）的模型的量子对应物。从计算角度来看，所提出的框架允许以量子电路深度线性增加为代价，生成指数级数量的不同输入变换。因此，MAQA产生了一个具有显著描述能力的模型，从而以超越经典方法的计算优势拓宽了量子机器学习可能的应用前景。作为第二项有意义的补充，我们讨论了将所提出的框架作为混合量子-经典和容错量子算法的应用。