ScholarGate
Assistent
Machine learningVariational Algorithm

Quantum Approximate Optimization Algorithm

Quantum Approximate Optimization Algorithm (QAOA) er en hybrid kvante-klassisk algoritme designet til at løse kombinatoriske optimeringsproblemer på kvantecomputere tilgængelige på kort sigt. Introduceret af Farhi, Goldstone og Gutmann i 2014, koder QAOA optimeringsproblemer i kvantekredsløb og anvender klassisk optimering til at finjustere kredsløbsparametre med det formål at finde tilnærmelsesvis optimale løsninger på problemer som MaxCut, graf-farvelægning og tidsplanlægning.

Åbn i MethodMindSnartVideoSnartDownload slides

Læs hele metoden

Kun for medlemmer

Log ind med en gratis konto for at læse dette afsnit.

Log ind

Method map

The neighbourhood of related methods — select a node to explore.

Kilder

  1. Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI: 10.48550/arXiv.1411.4028
  2. Zhou, L., Wang, S. T., Choi, S., et al. (2020). Quantum approximate optimization algorithm: Performance, mechanism, and implementation on near-term devices. Physical Review X, 10, 021067. DOI: 10.1103/PhysRevX.10.021067
  3. Hadfield, S., Wang, Z., O'Gorman, B., et al. (2019). From the Ising model to QAOA: A quantum optimization algorithm from the physicist's perspective. Algorithms, 12, 34. link

Sådan citerer du denne side

ScholarGate. (2026, June 3). Quantum Approximate Optimization Algorithm (QAOA). ScholarGate. https://scholargate.app/da/quantum-computing/quantum-approximate-optimization-algorithm

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

Compare side by side

Refereret af

ScholarGateQuantum Approximate Optimization Algorithm (Quantum Approximate Optimization Algorithm (QAOA)). Hentet 2026-06-15 fra https://scholargate.app/da/quantum-computing/quantum-approximate-optimization-algorithm · Datasæt: https://doi.org/10.5281/zenodo.20539026