Porównaj metody
Przeglądaj wybrane metody obok siebie; wiersze, które się różnią, są wyróżnione.
| Kwantowy Algorytm Przybliżonej Optymalizacji× | Estymacja Fazy Kwantowej× | |
|---|---|---|
| Dziedzina | Obliczenia kwantowe | Obliczenia kwantowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2014 | 1995 |
| Twórca≠ | Edward Farhi | Alexei Kitaev |
| Typ≠ | Hybrid quantum-classical algorithm | Subroutine algorithm |
| Źródło pierwotne≠ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ | Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026. link ↗ |
| Inne nazwy | QAOA, quantum alternating operator ansatz | QPE, phase kickback |
| Pokrewne≠ | 4 | 3 |
| Podsumowanie≠ | The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm designed to solve combinatorial optimization problems on near-term quantum devices. Introduced by Farhi, Goldstone, and Gutmann in 2014, QAOA encodes optimization problems into quantum circuits and uses classical optimization to tune circuit parameters, aiming to find approximately optimal solutions for problems like MaxCut, graph coloring, and scheduling. | Quantum Phase Estimation (QPE) is a fundamental quantum subroutine that estimates the eigenvalues of a unitary operator. Developed by Alexei Kitaev in 1995, QPE combines controlled unitary evolution with the quantum Fourier transform to extract eigenvalues from quantum states with exponential precision scaling. |
| ScholarGateZbiór danych ↗ |
|
|