Quantum Enhanced k-fold Cross-Validation

In this work, we propose a quantum-classical algorithm able to perform a k-fold cross-validation with linear speedup. The proposed method creates a quantum superposition with patterns from a dataset and a classifier can evaluate all patterns at once. We used a probabilistic quantum memory in order to conduct the performance evaluation. The proposed method was verified through a reduced experimental analysis conducted classically.