Recognizing the pattern of binary Hermitian matrices by quantum kNN and SVM methods

Abstract

Abstract The article contains a description of two quantum circuits for pattern recognition. The first approach is realized with use of k nearest neighbors algorithm and the second with support vector machine. The task is to distinguish between Hermitian and non-Hermitian matrices. The quantum circuits are constructed to accumulate elements of a learning set. After this process, circuits are able to produce a quantum state which contains the information if a tested element fits to the trained pattern. To improve the efficiency of presented solutions, the matrices were uniquely labeled with feature vectors. The role of the feature vectors is to highlight some features of the objects which are crucial in the process of classification. The circuits were implemented in Python programming language and some numeric experiments were conducted to examine the capacity of presented solutions in pattern recognition.

Keywords

• Quantum circuits
• Pattern recognition
• Supervised machine learning
• Hamming distance