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Advances in intelligent systems and computing

An Introduction to Quantum Machine Learning Algorithms

Rongji Li, Juan Xu, Jiabin Yuan, Dan Li

Published Jul 1, 2020• 6 citations
Publisher
DOI
10.1007/978-981-15-3753-0_51

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This paper presents an overview of quantum machine learning (QML) techniques, their approach, and their landscape. QML is a rapidly evolving field of study which has recently generated lots of interest due to improvements it promises over the classical machine learning approaches. This paper presents an overview of the application of quantum information theory on machine learning algorithms. It also discusses various designs, techniques, and algorithms of classical machine learning and quantum information theory and how they can be fused with each other. The goal of this paper is to analyze algorithms and techniques used in QML. The paper also presents some of the promising algorithms used in QML, which can be executed on a real quantum computer over cloud computing platforms with the aim to evaluate its performance based on given parameters.
Quantum Machine Learning (QML) is popularly known to be an integrative approach to learning of the Quantum Physics (QP) and Machine Learning (ML). In this chapter, an outline of the fundamental ideas and features related to quantum machine learning is laid out. The different facets of quantum algorithms are discussed in this chapter. In addition to this, the basic features of quantum reinforcement learning and quantum annealing are also provided in this chapter. Finally, the chapter deliberates about the advancement of quantum neural networks to through light in the direction of QML.
Quantum Machine Learning (QML) represents a paradigm shift in machine learning, leveraging quantum mechanics to address computational challenges and unlock new capabilities. This paper explores the application of adaptive gradient descent, a core technique in classical machine learning, to quantum machine learning algorithms. We propose a novel quantum gradient descent framework that dynamically adjusts the learning rate and optimization direction based on the input data's inherent features. This adaptive approach aims to significantly enhance training efficiency and model generalization performance. The core mechanism involves the introduction of a 'quantum gradient descent' process, utilizing quantum entanglement and superposition to accelerate gradient computation compared to classical methods. We analyze the potential benefits and challenges of this new algorithm, demonstrating its effectiveness through a simplified conceptual model.