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Quantum Neural Networks

McGill University_031422A
[McGill University]


- Overview

A quantum neural network (QNN) is a machine learning (ML) model or algorithm that combines concepts from quantum computing and artificial neural networks (ANNs).

QNNs are ML models that use quantum effects to perform computations. They are deployed on quantum computers and combine concepts from quantum computing and artificial neural networks (ANNs).

QNNs use quantum effects like superposition, entanglement, and interference to perform computations. Quantum computers hold great power because they are capable of faster computation than their classical counterparts. Some proposals for QNNs suggest potential advantages, such as faster processing and speed-ups in training. 

QNNs are developed as feed-forward networks. This structure takes input from one layer of qubits and passes it onto another layer of qubits. The layer of qubits then evaluates the information and passes the output to the next layer. 

One motivation for QNNs is the difficulty in training large classical neural networks, especially in big data applications. 


- Quantum Deep Learning

Quantum deep learning is a research field for the use of quantum computing techniques for training deep neural networks. The research topics and directions of deep learning and quantum computing have been separated for long time, however by discovering that quantum circuits can act like artificial neural networks, quantum deep learning research is widely adopted.


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