Quantum Machine Learning: Algorithms and Complexities

Quantum machine learning, also known as QML, is a blooming field of modern artificial intelligence that integrates quantum computing with machine learning. It aims to enhance traditional machine learning algorithms and develop novel computational methods.

This article examines the inner workings of quantum machine learning and related topics. It includes the fundamentals of quantum computing, quantum machine learning algorithms, the characteristics of quantum data, hybrid quantum-classical models, variational quantum algorithms, quantum-enhanced reinforcement learning, and the difficulties associated with quantum machine learning.

“The fusion of quantum computing and artificial intelligence, paving the way for groundbreaking innovation and endless opportunities.” – Sri Amit Ray

Quantum machine learning (QML) Algorithms

Quantum machine learning (QML) is a rapidly evolving field that explores the intersection of quantum computing and machine learning. Several algorithms and approaches have been proposed to leverage quantum resources for various machine learning tasks. The prominent quantum machine learning algorithms include:

Quantum Support Vector Machine (QSVM): QSVM is a quantum version of the classical support vector machine algorithm. It aims to classify data points by mapping them into a higher-dimensional feature space using a quantum kernel [4].Quantum k-Means Clustering: Quantum k-means is a quantum algorithm for clustering data points. It utilizes quantum resources to accelerate the k-means clustering process, which partitions data into k clusters [6].

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