Vertaile menetelmiä

	Variaatiokvanttilaskennan ratkaisija	Kvanttiaproksimatiivinen optimointialgoritmi
Tieteenala	Kvanttilaskenta	Kvanttilaskenta
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2014	2014
Kehittäjä	Alberto Peruzzo	Edward Farhi
Tyyppi	Hybrid quantum-classical algorithm	Hybrid quantum-classical algorithm
Alkuperäislähde	Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.	Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.
Rinnakkaisnimet	VQE, hybrid quantum-classical	QAOA, quantum alternating operator ansatz
Liittyvät	4	4
Tiivistelmä	The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue (ground state energy) of a quantum Hamiltonian. Introduced by Peruzzo et al. in 2014, it exploits the variational principle to combine the power of quantum circuits with classical optimization to solve chemistry and materials science problems on near-term quantum devices.	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.