Linganisha mbinu

	Mashineina ya Kujitenga ya Quantum	Algorithm ya Quantum Approximate Optimization
Nyanja	Ukokotoaji wa Kwantamu	Ukokotoaji wa Kwantamu
Familia	Machine learning	Machine learning
Mwaka wa asili	2014	2014
Mwanzilishi	Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd	Edward Farhi
Aina	Machine learning algorithm	Hybrid quantum-classical algorithm
Chanzo asilia	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.
Majina mbadala	QSVM, quantum kernel	QAOA, quantum alternating operator ansatz
Zinazohusiana	2	4
Muhtasari	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.