Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Квантовое оценивание фазы× | Квантовый приближенный оптимизационный алгоритм× | |
|---|---|---|
| Область | Квантовые вычисления | Квантовые вычисления |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1995 | 2014 |
| Автор метода≠ | Alexei Kitaev | Edward Farhi |
| Тип≠ | Subroutine algorithm | Hybrid quantum-classical algorithm |
| Основополагающий источник≠ | Kitaev, A. Y. (1995). Quantum measurements and the Abelian stabilizer problem. arXiv preprint quant-ph/9511026. link ↗ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ |
| Другие названия | QPE, phase kickback | QAOA, quantum alternating operator ansatz |
| Связанные≠ | 3 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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