A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance

• URL: http://arxiv.org/abs/2401.11351v2
• Date: Sun, 31 Mar 2024 00:32:13 GMT
• Title: A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
• Authors: Yunfei Wang, Junyu Liu,
• Abstract summary: We offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
• Score: 8.050429258747256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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