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	Quantum Approximate Optimization Algorithm	Quantum Monte Carlo
分野	量子コンピューティング	量子コンピューティング
系統	Machine learning	Machine learning
提唱年	2014	1953
提唱者	Edward Farhi	Nicholas Metropolis and colleagues
種類	Hybrid quantum-classical algorithm	Monte Carlo simulation
原典	Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028.	Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.
別名	QAOA, quantum alternating operator ansatz	QMC, variational Monte Carlo, diffusion Monte Carlo
関連	4	3
概要	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.	Quantum Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems.