A Leap among Quantum Computing and Quantum Neural Networks: A Survey
| Autor: | Fabio Valerio Massoli, Lucia Vadicamo, Giuseppe Amato, Fabrizio Falchi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Quantum Neural Network
FOS: Computer and information sciences Quantum Deep Learning Quantum Physics General Computer Science Computer Science - Emerging Technologies FOS: Physical sciences Machine Learning (stat.ML) I.2.0 Theoretical Computer Science Emerging Technologies (cs.ET) Statistics - Machine Learning ComputerSystemsOrganization_MISCELLANEOUS Quantum Computing Quantum Physics (quant-ph) Quantum Machine Learning |
| Zdroj: | ACM computing surveys (2022). doi:10.1145/3529756 |
| DOI: | 10.48550/arxiv.2107.03313 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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