Quantum Machine Learning in this NISQ Era is making progress in the problems related classification, categorisation, clustering, and optimisation areas for specific type of datasets in chemistry, finance, travel and transportation domain.
For example, the Microsoft Q# Quantum Machine Learning Toolkit uses Pauli X,and Z gates to construct a Classifier Algorithms titled Half Moon and Full Moon. The Pauli gates are the Single Qubit Gates based on the better-known Pauli matrices (i.e., Pauli spin matrices) which are incredibly useful for calculating changes to the spin of a single electron. Please find the implementation in this GitHub repository.
Tensorflow Quantum by Google has a collection of interesting quantum machine learning models for optimising gradient based algorithms. NetKet is a another collection of interesting quantum machine learning algorithms such as Quantum State Tomography for Quantum State Reconstruction. Please find a reference implementation from Netket in this GitHub repository.