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| Variationaler Quanten-Eigensolver× | Quantum Monte Carlo× | |
|---|---|---|
| Fachgebiet | Quantencomputing | Quantencomputing |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2014 | 1953 |
| Urheber≠ | Alberto Peruzzo | Nicholas Metropolis and colleagues |
| Typ≠ | Hybrid quantum-classical algorithm | Monte Carlo simulation |
| Wegweisende Quelle≠ | Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213. DOI ↗ | Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. DOI ↗ |
| Aliasnamen≠ | VQE, hybrid quantum-classical | QMC, variational Monte Carlo, diffusion Monte Carlo |
| Verwandt≠ | 4 | 3 |
| 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. | Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems. |
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