Quantum Machine Learning combines quantum computing with machine learning techniques to potentially solve certain computational problems faster or more efficiently than classical methods. It explores how quantum systems—based on principles like superposition and entanglement—can represent and manipulate data in fundamentally different ways.

QML algorithms typically fall into two categories: hybrid models, where quantum circuits are embedded into classical ML pipelines (e.g., variational quantum classifiers), and fully quantum algorithms like quantum support vector machines or quantum kernel methods. These models run on quantum hardware or simulators and are designed to work with limited qubit counts and noisy environments (NISQ era).

The hope is that QML can offer speedups in tasks like optimization, linear algebra, and sampling—core components of many ML problems. However, current applications are mostly experimental, with open challenges in scalability, noise, and data encoding.

QML is actively researched in fields like drug discovery, finance, and cryptography, where even modest speedups could have practical value once reliable quantum hardware becomes more accessible.