Порівняння методів

	Квантовий метод опорних векторів	Квантовий наближений алгоритм оптимізації	Варіаційний квантовий алгоритм знаходження власних значень
Галузь	Квантові обчислення	Квантові обчислення	Квантові обчислення
Родина	Machine learning	Machine learning	Machine learning
Рік появи	2014	2014	2014
Автор методу	Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd	Edward Farhi	Alberto Peruzzo
Тип	Machine learning algorithm	Hybrid quantum-classical algorithm	Hybrid quantum-classical algorithm
Основоположне джерело	Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503.	Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.	Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213.
Інші назви	QSVM, quantum kernel	QAOA, quantum alternating operator ansatz	VQE, hybrid quantum-classical
Пов'язані	2	4	4
Підсумок	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 Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm designed to solve combinatorial optimization problems on near-term quantum devices. Introduced by Farhi, Goldstone, and Gutmann in 2014, QAOA encodes optimization problems into quantum circuits and uses classical optimization to tune circuit parameters, aiming to find approximately optimal solutions for problems like MaxCut, graph coloring, and scheduling.	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.