Quantum neural network

Abstract

It is suggested that a quantum neural network (QNN), a type of artificial neural network, can be built using the principles of quantum information processing. The input and output qubits in the QNN can be implemented by optical modes with different polarization, the weights of the QNN can be implemented by optical beam splitters and phase shifters

LaTeX, 4 pages

Cited by in corpus (18)

• The quest for a Quantum Neural Network
• Simulating a perceptron on a quantum computer
• Quantum autoencoders to denoise quantum data
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Building quantum neural networks based on swap test
• Advances in Quantum Deep Learning: An Overview
• Variational Quantum Classifiers Through the Lens of the Hessian
• A Unitary Weights Based One-Iteration Quantum Perceptron Algorithm for Non-Ideal Training Sets
• A Simple Quantum Neural Net with a Periodic Activation Function
• Quantum kernels to learn the phases of quantum matter
• A review of Quantum Neural Networks: Methods, Models, Dilemma
• Weightless neural network parameters and architecture selection in a quantum computer
• Autonomous Quantum Perceptron Neural Network
• A Quantum Model for Multilayer Perceptron
• Qudit Machine Learning
• Quantum-enhanced neural networks in the neural tangent kernel framework
• A Quantum Implementation Model for Artificial Neural Networks
• Defining Quantum Neural Networks via Quantum Time Evolution