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{{short description|Quantum Mechanics in Neural Networks}}
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'''Quantum neural networks''' are [[Neural network (machine learning)|computational neural network]] models which are based on the principles of [[quantum mechanics]]. The first ideas on quantum neural computation were published independently in 1995 by [[Subhash Kak]] and Ron Chrisley,<ref>{{cite journal |first=S. |last=Kak |title=On quantum neural computing |journal=Advances in Imaging and Electron Physics |volume=94 |pages=259–313 |year=1995 |doi=10.1016/S1076-5670(08)70147-2 |isbn=9780120147366 }}</ref><ref>{{cite book |first=R. |last=Chrisley |chapter=Quantum Learning |title=New directions in cognitive science: Proceedings of the international symposium, Saariselka, 4–9 August 1995, Lapland, Finland |editor-first=P. |editor-last=Pylkkänen |editor2-first=P. |editor2-last=Pylkkö |publisher=Finnish Association of Artificial Intelligence |___location=Helsinki |pages=77–89 |year=1995 |isbn=951-22-2645-6 }}</ref> engaging with the theory of [[quantum mind]], which posits that quantum effects play a role in cognitive function. However, typical research in quantum neural networks involves combining classical [[artificial neural network]] models (which are widely used in machine learning for the important task of pattern recognition) with the advantages of [[quantum information]] in order to develop more efficient algorithms.<ref>{{cite journal|last1=da Silva|first1=Adenilton J.|last2=Ludermir|first2=Teresa B.|last3=de Oliveira|first3=Wilson R.|year=2016|title=Quantum perceptron over a field and neural network architecture selection in a quantum computer|journal=Neural Networks|volume=76|pages=55–64|arxiv=1602.00709|bibcode=2016arXiv160200709D|doi=10.1016/j.neunet.2016.01.002|pmid=26878722|s2cid=15381014}}</ref><ref>{{cite journal|last1=Panella|first1=Massimo|last2=Martinelli|first2=Giuseppe|year=2011|title=Neural networks with quantum architecture and quantum learning|journal=[[International Journal of Circuit Theory and Applications]]|volume=39|pages=61–77|doi=10.1002/cta.619|s2cid=3791858 }}</ref><ref>{{cite journal |first1=M. |last1=Schuld |first2=I. |last2=Sinayskiy |first3=F. |last3=Petruccione |arxiv=1408.7005 |title=The quest for a Quantum Neural Network |journal=Quantum Information Processing |volume=13 |issue=11 |pages=2567–2586 |year=2014 |doi=10.1007/s11128-014-0809-8 |bibcode=2014QuIP...13.2567S |s2cid=37238534 }}</ref> One important motivation for these investigations is the difficulty to train classical neural networks, especially in [[Big data|big data applications]]. The hope is that features of [[quantum computing]] such as [[quantum parallelism]] or the effects of [[quantum interference|interference]] and [[Quantum entanglement|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.
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