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