Performance Analysis and Noise Impact of a Novel Quantum KNN Algorithm for Machine Learning

TL;DR

This paper introduces a novel quantum KNN algorithm that leverages quantum computing techniques to improve classification accuracy, scalability, and noise robustness, demonstrating superior performance on benchmark datasets compared to classical methods.

Contribution

The paper proposes a new quantum KNN algorithm with optimized data encoding, enhanced feature extraction, a quantum distance metric, and noise mitigation strategies, advancing quantum machine learning capabilities.

Findings

Achieves higher accuracy than classical k-NN and QNN on benchmark datasets.

Introduces a quantum distance metric based on the swap test for better similarity measurement.

Incorporates noise mitigation techniques to ensure stability in quantum environments.

Abstract

This paper presents a novel quantum K-nearest neighbors (QKNN) algorithm, which offers improved performance over the classical k-NN technique by incorporating quantum computing (QC) techniques to enhance classification accuracy, scalability, and robustness. The proposed modifications focus on optimizing quantum data encoding using Hadamard and rotation gates, ensuring more effective rendering of classical data in quantum states. In addition, the quantum feature extraction process is significantly enhanced by the use of entangled gates such as IsingXY and CNOT, which enables better feature interactions and class separability. A novel quantum distance metric, based on the swap test, is introduced to calculate similarity measures between various quantum states, offering superior accuracy and computational efficiency compared to traditional Euclidean distance metrics. We assess the achievement of the proposed QKNN algorithm on three benchmark datasets: Wisconsin Breast Cancer, Iris, and Bank Note Authentication, and have noted its superior performance relative to both classical k-NN (CKNN) and Quantum Neural Network (QNN). The proposed QKNN algorithm is found to achieve prediction accuracies of 98.25%, 100%, and 99.27% ,respectively, for the three datasets, while the customized QNN shows prediction accuracies of only 97.17%, 83.33%, and 86.18%, respectively. Furthermore, we address the challenges of quantum noise by incorporating a repetition encoding-based error mitigation strategy, which ensures the stability and resilience of the algorithm in noisy quantum environments. The results highlight the potential of the proposed QKNN as a scalable, efficient and robust quantum-enhanced machine learning algorithm, especially in high-dimensional and complex datasets, when traditional approaches frequently fail.