This review provides an overview of machine learning concepts and techniques, with a focus on the potential applications of quantum machine learning. It covers the following key points:
Background on artificial intelligence, machine learning, and the relationship between them. The review introduces concepts like supervised and unsupervised learning, hypothesis spaces, loss functions, and overfitting.
Examples of classical machine learning algorithms, including linear regression, clustering (K-Means), support vector machines, and deep learning. The review explains the underlying mathematics and intuitions behind these techniques.
Discussions on the potential benefits of quantum machine learning, such as faster training and the identification of feature maps not found classically. However, the review also highlights the current challenges of using quantum computers for machine learning, including the limitations of Noisy Intermediate Scale Quantum (NISQ) devices and the lack of fault-tolerant quantum computers.
Specific examples of quantum machine learning applications that have been demonstrated on contemporary quantum devices, including image classification, toxicity screening, and probability distribution learning.
Insights on the connections between classical kernel methods and the quantum inner product, as well as the concept of a "quantum kernel classifier" that could potentially be realized on NISQ machines.
Overall, the review provides a solid introduction to machine learning and the current state of quantum machine learning, serving as a useful resource for those with a background in quantum computing who wish to explore the field of quantum machine learning.
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by Daniel Golds... alle arxiv.org 04-30-2024
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