Quantum K-nearest neighbor classification algorithm based on Hamming distance

Abstract

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines the sample's category by the similarity between samples. In this paper, we propose a quantum K-nearest neighbor classification algorithm with Hamming distance. In this algorithm, quantum computation is firstly utilized to obtain Hamming distance in parallel. Then, a core sub-algorithm for searching the minimum of unordered integer sequence is presented to find out the minimum distance. Based on these two sub-algorithms, the whole quantum frame of K-nearest neighbor classification algorithm is presented. At last, it is shown that the proposed algorithm can achieve a quadratical speedup by analyzing its time complexity briefly.

8 pages,5 figures

References in corpus (4)

• Quantum algorithm for solving linear systems of equations
• Quantum random access memory
• Architectures for a quantum random access memory
• Experimental Realization of Quantum Artificial Intelligence

Cited by in corpus (8)

• Quantum Machine Learning for Finance
• Strong photon antibunching effect in a double cavity optomechanical system with intracavity squeezed light
• A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation
• Quantum Gaussian process regression
• Forecasting steam mass flow in power plants using the parallel hybrid network
• Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification
• Block Encoding of Sparse Matrices via Coherent Permutation