Comparar métodos

	Estimación de Fase Cuántica	Algoritmo Cuántico Aproximado de Optimización
Campo	Computación cuántica	Computación cuántica
Familia	Machine learning	Machine learning
Año de origen	1995	2014
Autor original	Alexei Kitaev	Edward Farhi
Tipo	Subroutine algorithm	Hybrid quantum-classical algorithm
Fuente seminal	Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026.	Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.
Alias	QPE, phase kickback	QAOA, quantum alternating operator ansatz
Relacionados	3	4
Resumen	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.