Document - A leap among quantum computing and quantum neural networks: a survey

2022

Journal article Open Access

A leap among quantum computing and quantum neural networks: a survey

Massoli F. V., Vadicamo L., Amato G., Falchi F.

Quantum Computing Statistics - Machine Learning Machine Learning (stat.ML) General Computer Science Quantum Machine Learning Quantum Physics Computer Science - Emerging Technologies Quantum Physics (quant-ph) FOS: Physical sciences Quantum Deep Learning FOS: Computer and information sciences I.2.0 Theoretical Computer Science Quantum Neural Network Emerging Technologies (cs.ET)

In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community's interest since the late 80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.

Source: ACM computing surveys (2022). doi:10.1145/3529756

Publisher: Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America

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@article{oai:it.cnr:prodotti:472061,
	title = {A leap among quantum computing and quantum neural networks: a survey},
	author = {Massoli F. V. and Vadicamo L. and Amato G. and Falchi F.},
	publisher = {Association for Computing Machinery,, New York, N.Y. , Stati Uniti d'America},
	doi = {10.1145/3529756 and 10.48550/arxiv.2107.03313},
	journal = {ACM computing surveys},
	year = {2022}
}