A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
It synthesizes existing knowledge for researchers and practitioners in quantum computing and machine learning, but is incremental as a review.
The paper provides a comprehensive review of quantum machine learning, covering techniques from NISQ to fault-tolerant computing, including fundamental concepts, algorithms, and statistical learning theory.
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.