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Quantum neural network
Quantum neural networks (QNNs) are neural network models which are based on the principles of quantum mechanics. There are two different approaches to QNN research, one exploiting quantum information processing to improve existing neural network models (sometimes also vice versa), and the other one searching for potential quantum effects in the brain.
== Artificial quantum neural networks ==
In the computational approach to quantum neural network research, scientists try to combine artificial neural network models (which are widely used in machine learning for the important task of pattern classification) with the advantages of quantum information in order to develop more efficient algorithms (for a review, see 〔M. Schuld, I. Sinayskiy, F. Petruccione: The quest for a Quantum Neural Network, Quantum Information Processing 13, 11 , pp. 2567-2586 (2014)〕). One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources. Since the technological implementation of a quantum computer is still in a premature stage, such quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments.
Quantum neural network research is still in its infancy, and a conglomeration of proposals and ideas of varying scope and mathematical rigourosity have been put forward. Most of them are based on the idea of replacing classical binary or McCulloch-Pitts neurons with a qubit (which can be called a “quron”), resulting in neural units that can be in a superposition of the state ‘firing’ and ‘resting’.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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