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Quantum neural network — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Quantum_neural_network
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-06T08:54:58.488Z
SHA-256 of the archived text: 6370bf0a15b94ba57fbfbfa6af6494cfe7003718087b7000ab71e0dde8e3b844
Stable URL of this page: https://metraipolis.com/documento/dc_cb6300a94b2f

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Quantum neural network — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Quantum_neural_network (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_cb6300a94b2f (Metraipolis archive dc_cb6300a94b2f, sha256 6370bf0a15b94ba57fbfbfa6af6494cfe7003718087b7000ab71e0dde8e3b844)

BibTeX

@misc{dccb6300a94b2f,
  title = {Quantum neural network — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Quantum_neural_network},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_cb6300a94b2f; sha256 6370bf0a15b94ba57fbfbfa6af6494cfe7003718087b7000ab71e0dde8e3b844},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Quantum_neural_network. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Quantum neural networks (QNN) are 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, 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. One important motivation for these investigations is the difficulty to train classical neural networks, especially in big data applications. The hope is that features of quantum computing such as quantum parallelism or the effects of interference and 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. Most quantum neural networks are developed as feed-forward networks. Similar to their classical counterparts, this structure intakes input from one layer of qubits, and passes that input onto another layer of qubits. This layer of qubits evaluates this information and passes on the output to the next layer. Eventually the path leads to the final layer of qubits. The layers do not have to be of the same width, meaning they don't have to have the same number of qubits as the layer before or after it. This structure is trained on which path to take similar to classical artificial neural networks. This is discussed in a lower section. Quantum neural networks refer to three different categories: Quantum computer with classical data, classical computer with quantum data, and quantum computer with quantum data.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «artificial neural network»).
Fonte: Wikipedia in inglese, voce «Quantum neural network» — https://en.wikipedia.org/wiki/Quantum_neural_network
Wikidata Q1981341 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q1981341
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Quantum_neural_network?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1376377741, consultata il 2026-10-06T08:20:49.289Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 8f14b6141f79deb413f4e83951c472695c75851963b5b230f8fd580ce2e26c44