Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Квантовый приближенный оптимизационный алгоритм× | Квантовое оценивание фазы× | |
|---|---|---|
| Область | Квантовые вычисления | Квантовые вычисления |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2014 | 1995 |
| Автор метода≠ | Edward Farhi | Alexei Kitaev |
| Тип≠ | Hybrid quantum-classical algorithm | Subroutine algorithm |
| Основополагающий источник≠ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ | Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026. link ↗ |
| Другие названия | QAOA, quantum alternating operator ansatz | QPE, phase kickback |
| Связанные≠ | 4 | 3 |
| Сводка≠ | 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 Phase Estimation (QPE) is a fundamental quantum subroutine that estimates the eigenvalues of a unitary operator. Developed by Alexei Kitaev in 1995, QPE combines controlled unitary evolution with the quantum Fourier transform to extract eigenvalues from quantum states with exponential precision scaling. |
| ScholarGateНабор данных ↗ |
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