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| Kwantowy Algorytm Przybliżonej Optymalizacji× | Algorytm Grovera× | |
|---|---|---|
| Dziedzina | Obliczenia kwantowe | Obliczenia kwantowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2014 | 1996 |
| Twórca≠ | Edward Farhi | Lov Grover |
| Typ≠ | Hybrid quantum-classical algorithm | Quantum algorithm |
| Źródło pierwotne≠ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ | Grover, L. K. (1996). A fast quantum mechanical algorithm for database search. Proceedings of the 28th Annual ACM Symposium on Theory of Computing (STOC), 212–219. DOI ↗ |
| Inne nazwy | QAOA, quantum alternating operator ansatz | quantum search, amplitude amplification |
| 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. | Grover's Algorithm is a quantum algorithm for searching an unsorted database, offering a quadratic speedup over classical linear search. Proposed by Lov Grover in 1996, it exploits quantum superposition and amplitude amplification to find a target item among N items in O(√N) queries, compared to the classical O(N) requirement. |
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