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Machine learning in physics — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Machine_learning_in_physics
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-06T00:19:43.013Z
SHA-256 of the archived text: 274b5e6da2ff97db6a91f907c28cee16de7610e15ef4100cd4ab5fb89084a806
Stable URL of this page: https://metraipolis.com/documento/dc_263bf55aa39f

How to cite

Machine learning in physics — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Machine_learning_in_physics (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_263bf55aa39f (Metraipolis archive dc_263bf55aa39f, sha256 274b5e6da2ff97db6a91f907c28cee16de7610e15ef4100cd4ab5fb89084a806)

BibTeX

@misc{dc263bf55aa39f,
  title = {Machine learning in physics — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Machine_learning_in_physics},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_263bf55aa39f; sha256 274b5e6da2ff97db6a91f907c28cee16de7610e15ef4100cd4ab5fb89084a806},
  year = {2026}
}

Archived text

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

Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians,, detecting phase transition in spin-systems even when not trained on physical configurations near criticality, learning quantum phase transitions, and automatically generating new quantum experiments. ML is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technology development, and computational materials design. In this context, for example, it can be used as a tool to interpolate pre-calculated interatomic potentials, or directly solving the Schrödinger equation with a variational method.

Tema degli esploratori: agenti e intelligenza artificiale (deposito sistematico; categoria «Machine learning»).
Fonte: Wikipedia in inglese, voce «Machine learning in physics» — https://en.wikipedia.org/wiki/Machine_learning_in_physics
Wikidata Q83512991 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q83512991
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Machine_learning_in_physics?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 1372985665, consultata il 2026-10-05T23:36:36.486Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: b84f879d61346fccf0783fd0eca959ed91966a31ceef5520cc9a0492cb84c3a9