Comparar métodos

	Máquina de Vetores de Suporte Quântica	Variational Quantum Eigensolver
Área	Computação quântica	Computação quântica
Família	Machine learning	Machine learning
Ano de origem	2014	2014
Autor original	Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd	Alberto Peruzzo
Tipo	Machine learning algorithm	Hybrid quantum-classical algorithm
Fonte seminal	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.
Outros nomes	QSVM, quantum kernel	VQE, hybrid quantum-classical
Relacionados	2	4
Resumo	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.