Quantum Machine Learning

Quantum Machine Learning (QML) combines quantum computing principles with machine learning algorithms to potentially achieve computational advantages over classical machine learning approaches.

Quantum Machine Learning leverages quantum mechanical phenomena like superposition and entanglement to process information in ways that classical computers cannot. This emerging field explores how quantum algorithms can enhance or accelerate machine learning tasks, whilst also investigating how machine learning can assist in solving quantum computing challenges.

The potential advantages of QML include faster processing of complex datasets, ability to handle higher-dimensional data spaces, and improved pattern recognition capabilities. However, the field is still in its early stages, with researchers working to demonstrate practical quantum advantages over classical methods in real-world applications.

Examples

• Optimisation problems in finance
• Drug discovery and molecular modelling
• Pattern recognition in big data
• Quantum state classification
• Portfolio optimisation in trading