विधियों की तुलना करें

	वैरिएशनल क्वांटम आइजेनसाॅल्वर	क्वांटम एप्रोक्सिमेट ऑप्टिमाइजेशन एल्गोरिथम
क्षेत्र	क्वांटम कंप्यूटिंग	क्वांटम कंप्यूटिंग
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2014	2014
प्रवर्तक	Alberto Peruzzo	Edward Farhi
प्रकार	Hybrid quantum-classical algorithm	Hybrid quantum-classical algorithm
मौलिक स्रोत	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.
उपनाम	VQE, hybrid quantum-classical	QAOA, quantum alternating operator ansatz
संबंधित	4	4
सारांश	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.