Methoden vergleichen

	Variationaler Quanten-Eigensolver	Dichtefunktionaltheorie
Fachgebiet	Quantencomputing	Quantencomputing
Familie	Machine learning	Machine learning
Entstehungsjahr	2014	1965
Urheber	Alberto Peruzzo	Walter Kohn
Typ	Hybrid quantum-classical algorithm	Electronic structure method
Wegweisende Quelle	Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.	Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.
Aliasnamen	VQE, hybrid quantum-classical	DFT, Kohn-Sham equations
Verwandt	4	4
Zusammenfassung	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.	Density Functional Theory (DFT) is a computational method for determining the properties of materials and molecules by modeling the ground state electron density. Developed by Walter Kohn and Lu Jeu Sham in the 1960s, DFT reduces the complexity of quantum chemistry from tracking individual electron coordinates to optimizing the total electron density, enabling efficient simulations of large molecular and condensed-matter systems.