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Quantum neural networks are based on the principles of Quantum mechanics

    Quantum neural networks are computational neural network models which are based on the principles of quantum mechanics. The first ideas were published independently in 1995 by Subhash Kak and Ron Chrisley, engaging with quantum mind – which posits quantum effects play a role in cognitive function.

    Quantum neural network models are mostly theoretical proposals that await their full implementation in physical experiments. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and entanglement can be used as resources.

    Most Quantum neural networks are developed as feed-forward networks. This structure intakes input from one layer of qubits, and passes that input onto the next layer. Eventually the path leads to the final layer, which is the same width as the layer before or after it.

    Source:

    [1] Wikipedia Contributors. “Quantum Neural Network.” Wikipedia, Wikimedia Foundation, 13 Dec. 2020, en.wikipedia.org/wiki/Quantum_neural_network. Accessed 8 Jan. 2021.

    ‌[2]  TheDigitalArtist. “Eye Sight Vision – Free Image on Pixabay.” Pixabay.com, 25 Aug. 2016, pixabay.com/illustrations/eye-sight-vision-eyesight-lens-1616986/. Accessed 8 Jan. 2021.

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