Comparer des méthodes

	Machine à vecteurs de support quantique	Algorithme variationnel quantique d'estimation d'énergie
Domaine	Informatique quantique	Informatique quantique
Famille	Machine learning	Machine learning
Année d'origine	2014	2014
Auteur d'origine	Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd	Alberto Peruzzo
Type	Machine learning algorithm	Hybrid quantum-classical algorithm
Source fondatrice	Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503.	Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.
Alias	QSVM, quantum kernel	VQE, hybrid quantum-classical
Apparentées	2	4
Résumé	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.