পদ্ধতির তুলনা করুন

	কোয়ান্টাম সাপোর্ট ভেক্টর মেশিন	কোয়ান্টাম অ্যাপ্রক্সিমেট অপ্টিমাইজেশন অ্যালগরিদম	ভ্যারিয়েশনাল কোয়ান্টাম আইগেনসলভার
ক্ষেত্র	কোয়ান্টাম কম্পিউটিং	কোয়ান্টাম কম্পিউটিং	কোয়ান্টাম কম্পিউটিং
পরিবার	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	2014	2014	2014
প্রবর্তক	Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd	Edward Farhi	Alberto Peruzzo
ধরন	Machine learning algorithm	Hybrid quantum-classical algorithm	Hybrid quantum-classical algorithm
মৌলিক উৎস	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.
অপর নাম	QSVM, quantum kernel	QAOA, quantum alternating operator ansatz	VQE, hybrid quantum-classical
সম্পর্কিত	2	4	4
সারসংক্ষেপ	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.