Сравнение методов

	Метод Монте-Карло на основе интегралов по траекториям	Теория функционала плотности
Область	Квантовые вычисления	Квантовые вычисления
Семейство	Machine learning	Machine learning
Год появления	1948	1965
Автор метода	Richard Feynman	Walter Kohn
Тип	Stochastic simulation	Electronic structure method
Основополагающий источник	Feynman, R. P. (1948). Space-time approach to non-relativistic quantum mechanics. Reviews of Modern Physics, 20, 367–387.	Kohn, W., Sham, L. J. (1965). Self-consistent equations including exchange and correlation effects. Physical Review, 140, A1133–A1138.
Другие названия	PIMC, Feynman path integral	DFT, Kohn-Sham equations
Связанные	3	4
Сводка	Path Integral Monte Carlo (PIMC) is a computational method for calculating thermodynamic and structural properties of quantum systems using Feynman's path integral formulation. Developed rigorously by David Ceperley and colleagues in the 1990s, PIMC treats quantum particles as classical polymers in a higher-dimensional space, enabling efficient Monte Carlo sampling of quantum statistics.	Density Functional Theory (DFT) is a computational method for determining the properties of materials and molecules by modeling the ground state electron density. Developed by Walter Kohn and Lu Jeu Sham in the 1960s, DFT reduces the complexity of quantum chemistry from tracking individual electron coordinates to optimizing the total electron density, enabling efficient simulations of large molecular and condensed-matter systems.