Quantum Computing and Quantum Machine Learning Tutorial Series

Quantum machine learning (QML) has attracted increasing interest as quantum computing advances toward practical learning and data-processing applications. At the same time, deploying QML in realistic settings raises new questions beyond predictive performance, including data privacy, distributed learning, communication, and the interaction between quantum resources and classical learning systems.

These tutorials introduce the foundations of quantum computing and QML, followed by an in-depth discussion of emerging topics such as privacy-preserving QML and quantum federated learning (QFL). We will review representative models and methodologies, discuss their connections to classical machine learning and privacy-preserving learning, and highlight key theoretical and practical challenges.

The tutorial is intended to provide researchers and practitioners with a structured entry point into QML while also identifying promising directions for future research.