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| 量子相位估计× | 量子近似优化算法× | |
|---|---|---|
| 领域 | 量子计算 | 量子计算 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1995 | 2014 |
| 提出者≠ | Alexei Kitaev | Edward Farhi |
| 类型≠ | Subroutine algorithm | Hybrid quantum-classical algorithm |
| 开创性文献≠ | 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 ↗ |
| 别名 | QPE, phase kickback | QAOA, quantum alternating operator ansatz |
| 相关≠ | 3 | 4 |
| 摘要≠ | 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. |
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