Quantum k-Nearest Neighbour Classifier: Investigating Hybrid Feature map approach in Quantum Machine Learning utilizing Pennylane

The k-nearest neighbor algorithm (k-NN) is a straightforward and highly effective classical machine learning method that categorises unidentified test instances by identifying the k-nearest Neighbors from the collection of m-training instances. It is widely acknowledged that integrating machine learning algorithms with quantum computing offers tremendous benefits in some applications involving higher dimensional datasets. This research examines the quantum k-nearest neighbor (QK-NN) algorithm executed within the PennyLane’s default. qubit framework. The paper assesses the benefits of incorporating quantum computing into machine learning problems by focusing on quantum circuits and quantum feature maps for quantum-enhanced classifiers. The PennyLane framework is employed to model the behaviour on four datasets viz. Iris, Wine, Rain, Indian Lever. The implementation results reveal that quantum-enhanced k-NN Combined feature maps exhibit superior performance on most of the Accuracy, Precision, Recall and F-1 score parameters compared to individual feature maps.