Quantum Neural Networks

Summary

Quantum neural networks (QNNs) are a burgeoning field that merges the principles of quantum mechanics with machine learning. These models aim to leverage the unique capabilities of quantum computers, such as superposition, entanglement, and interference, to overcome limitations faced by classical neural networks (NNs). Quantum machine learning algorithms often produce probabilistic results and contain correlated components. This chapter explores a general method for building NNs inspired by classical architectures on quantum computers. It delves into the key differences and similarities between quantum and classical NNs and also explores the potential of quantum‐classical hybrid approaches. The chapter also discusses some potential applications and future trends in the exciting field of QNNs. Artificial neurons are the building blocks of artificial neural network (ANN). Reinforcement learning is a powerful branch of machine learning where agents learn to make optimal decisions through a feedback mechanism of rewards and penalties.