Quantum [formula omitted]-fold cross-validation for nearest neighbor classification algorithm

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Cross-validation is one of the important tools in machine learning, which is generally used for performance evaluation. It uses different portions of the data to test and train a model on different iterations, which leads to a high computational cost. In this paper, we present a quantum version of k-fold cross-validation to choose a good parameter for the nearest neighbor classification algorithm with a threshold t, where the classification performance is estimated efficiently. With the help of amplitude amplification and estimation, the proposed quantum algorithm achieves a polynomial speedup on the number of samples over its classical counterpart.