Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Born-Oppenheimer Approximation× | Variationele Kwantum-Eigensolver× | |
|---|---|---|
| Vakgebied | Kwantumcomputing | Kwantumcomputing |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1927 | 2014 |
| Grondlegger≠ | Max Born and Julius Robert Oppenheimer | Alberto Peruzzo |
| Type≠ | Fundamental approximation | Hybrid quantum-classical algorithm |
| Oorspronkelijke bron≠ | Born, M., Oppenheimer, J. R. (1927). Zur Quantentheorie der Moleküle. Annalen der Physik, 84, 457–484. DOI ↗ | Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213. DOI ↗ |
| Aliassen | BO approximation, clamped nuclei | VQE, hybrid quantum-classical |
| Verwant≠ | 3 | 4 |
| Samenvatting≠ | The Born-Oppenheimer (BO) Approximation is a foundational assumption in molecular quantum mechanics that nuclei can be treated as fixed while solving for electrons, and vice versa. Introduced by Born and Oppenheimer in 1927, this separation reduces the complex many-body electronic-nuclear problem to a sequence of simpler problems, enabling nearly all molecular calculations. | 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. |
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