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HHL algorithm — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/HHL_algorithm
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-07T13:49:09.459Z
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HHL algorithm — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/HHL_algorithm (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_e545aabef148 (Metraipolis archive dc_e545aabef148, sha256 08aad5be326ae07646b36ab1b48c90b33e5b4ef470646bb583d0d08e990b6c98)

BibTeX

@misc{dce545aabef148,
  title = {HHL algorithm — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/HHL_algorithm},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_e545aabef148; sha256 08aad5be326ae07646b36ab1b48c90b33e5b4ef470646bb583d0d08e990b6c98},
  year = {2026}
}

Archived text

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

The Harrow–Hassidim–Lloyd (HHL) algorithm is a quantum algorithm for obtaining certain limited information about the solution to a system of linear equations, introduced by Aram Harrow, Avinatan Hassidim, and Seth Lloyd. Specifically, the algorithm estimates quadratic functions of the solution vector to a given system. The algorithm is one of the main fundamental algorithms expected to provide a speedup over their classical counterparts, along with Shor's factoring algorithm and Grover's search algorithm. Assuming the system is sparse, has a low condition number κ {\displaystyle \kappa } , and that the user is only interested in certain information about solution vector and not the entire vector itself, the algorithm has a runtime of O ( log ( N ) κ 2 ) {\displaystyle O(\log(N)\kappa ^{2})} , where N {\displaystyle N} is the number of variables. This offers an exponential speedup over the fastest classical algorithm, which runs in O ( N κ ) {\displaystyle O(N\kappa )} (or O ( N κ ) {\displaystyle O(N{\sqrt {\kappa }})} for positive semidefinite matrices). An implementation of the HHL algorithm was first demonstrated in 2013 by three independent publications, consisting of simple systems on specially designed devices. The first demonstration of a general-purpose version of the algorithm appeared in 2018.

Tema degli esploratori: statistica e scienza dei dati (deposito sistematico; ricerca «algorithm»).
Fonte: Wikipedia in inglese, voce «HHL algorithm» — https://en.wikipedia.org/wiki/HHL_algorithm
Wikidata Q17083575 (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q17083575
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/HHL_algorithm?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 1378875664, consultata il 2026-10-07T13:20:46.365Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: 3ac906c230ce09ca60b581acb3b04851bd866eb3d2eaa4fc21df1771dd46a1fb