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

TL;DR

This paper provides a comprehensive review of quantum machine learning, covering techniques from NISQ devices to fault-tolerant quantum algorithms, including fundamental concepts, algorithms, and statistical learning theory.

Contribution

It offers an unbiased, detailed overview of the field, highlighting recent developments and the transition from NISQ to fault-tolerant quantum computing approaches.

Findings

Summarizes key quantum machine learning algorithms.

Analyzes the challenges of NISQ and fault-tolerant quantum hardware.

Provides insights into the theoretical foundations of quantum learning.

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.