AI Article Synopsis
- Quantum machine learning combines machine learning with quantum computing, attracting interest from academics and businesses alike.
- This paper provides a thorough review of key concepts and techniques related to quantum machine learning, focusing on both Noisy Intermediate-Scale Quantum (NISQ) technologies and fault-tolerant algorithms.
- The review addresses foundational ideas, specific algorithms, and relevant statistical learning theories in the context of quantum machine learning.
Article Abstract
Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
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| http://dx.doi.org/10.1088/1361-6633/ad7f69 | DOI Listing |
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