Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmo Cuántico Aproximado de Optimización× | Algoritmo de Grover× | |
|---|---|---|
| Campo | Computación cuántica | Computación cuántica |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2014 | 1996 |
| Autor original≠ | Edward Farhi | Lov Grover |
| Tipo≠ | Hybrid quantum-classical algorithm | Quantum algorithm |
| Fuente seminal≠ | 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 ↗ |
| Alias | QAOA, quantum alternating operator ansatz | quantum search, amplitude amplification |
| Relacionados≠ | 4 | 3 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
|
|