Vertaile menetelmiä

	Kvanttiaproksimatiivinen optimointialgoritmi	Kvanttifaseestimaatio
Tieteenala	Kvanttilaskenta	Kvanttilaskenta
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2014	1995
Kehittäjä	Edward Farhi	Alexei Kitaev
Tyyppi	Hybrid quantum-classical algorithm	Subroutine algorithm
Alkuperäislähde	Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.	Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026.
Rinnakkaisnimet	QAOA, quantum alternating operator ansatz	QPE, phase kickback
Liittyvät	4	3
Tiivistelmä	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.