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| Estymacja Fazy Kwantowej× | Kwantowy Algorytm Przybliżonej Optymalizacji× | |
|---|---|---|
| Dziedzina | Obliczenia kwantowe | Obliczenia kwantowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1995 | 2014 |
| Twórca≠ | Alexei Kitaev | Edward Farhi |
| Typ≠ | Subroutine algorithm | Hybrid quantum-classical algorithm |
| Źródło pierwotne≠ | Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026. link ↗ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ |
| Inne nazwy | QPE, phase kickback | QAOA, quantum alternating operator ansatz |
| Pokrewne≠ | 3 | 4 |
| Podsumowanie≠ | 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. | 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. |
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