Methoden vergleichen

	Quantenschätzverfahren für Phasen	Quanten-Approximations-Optimierungsalgorithmus
Fachgebiet	Quantencomputing	Quantencomputing
Familie	Machine learning	Machine learning
Entstehungsjahr	1995	2014
Urheber	Alexei Kitaev	Edward Farhi
Typ	Subroutine algorithm	Hybrid quantum-classical algorithm
Wegweisende Quelle	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.
Aliasnamen	QPE, phase kickback	QAOA, quantum alternating operator ansatz
Verwandt	3	4
Zusammenfassung	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.