Quantum Machine Learning

This chapter provides a brief description of quantum machine learning (QML) and its correlation with artificial intelligence. It shows how the quantum counterpart of machine learning (ML) is much faster and more efficient than classical ML. In the QML techniques, the chapter develops quantum algorithms to operate the classical algorithms on a quantum computer. The quantum decision tree employs quantum states to create the classifiers used in ML. The current generation of quantum computing technologies calls for quantum algorithms that require a limited number of qubits and quantum gates, and that are robust against errors. The chapter discusses a low‐depth variational quantum algorithm for supervised learning. The parameter‐shift rule is an approach to evaluating gradients of parameterized quantum circuits on quantum hardware. The chapter introduces a quantum neural network that can represent labeled data – classical or quantum – and be trained by supervised learning.