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| Kvantstödvektor-maskin× | Variationskvanteigensolver× | |
|---|---|---|
| Ämnesområde | Kvantdatorteknik | Kvantdatorteknik |
| Familj | Machine learning | Machine learning |
| Ursprungsår | 2014 | 2014 |
| Upphovsperson≠ | Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd | Alberto Peruzzo |
| Typ≠ | Machine learning algorithm | Hybrid quantum-classical algorithm |
| Ursprungskälla≠ | Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503. DOI ↗ | Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213. DOI ↗ |
| Alias | QSVM, quantum kernel | VQE, hybrid quantum-classical |
| Närliggande≠ | 2 | 4 |
| Sammanfattning≠ | Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm combining quantum feature spaces with classical SVM training. Proposed by Rebentrost et al. in 2014, QSVM leverages quantum processors to compute kernel functions, potentially offering speedup for classification problems while remaining practical on near-term quantum devices. | 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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