Quantum Machine Learning

Quantum Algorithms

Quantum Machine Learning

Machine learning algorithms typically consist of three key components.  These are representation, evaluation, and optimization.  These components are iterated to improve performance.  

• Representation: the architecture the algorithm uses to represent knowledge; consists of rules, decision trees, neural networks, etc.; train the algorithm with examples with the goal of predicting; 
• Evaluation: function to evaluate algorithm  parameterizations; i.e. accuracy, prediction, cost, margin, etc.; how well the algorithm performs;
• Optimization: process to generate algorithm parameterizations; combinatorial, constrained, etc.; the search process; adjusts parameters to improve performance;