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| 양자 근사 최적화 알고리즘× | 변분 양자 고유값 해법× | |
|---|---|---|
| 분야 | 양자컴퓨팅 | 양자컴퓨팅 |
| 계열 | Machine learning | Machine learning |
| 기원 연도 | 2014 | 2014 |
| 창시자≠ | Edward Farhi | Alberto Peruzzo |
| 유형 | Hybrid quantum-classical algorithm | Hybrid quantum-classical algorithm |
| 원전≠ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ | Peruzzo, A., McClean, J., Shadbolt, P., et al. (2014). A variational eigenvalue solver on a photonic quantum processor. Nature Communications, 5, 4213. DOI ↗ |
| 별칭 | QAOA, quantum alternating operator ansatz | VQE, hybrid quantum-classical |
| 관련 | 4 | 4 |
| 요약≠ | 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. | The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to find the lowest eigenvalue (ground state energy) of a quantum Hamiltonian. Introduced by Peruzzo et al. in 2014, it exploits the variational principle to combine the power of quantum circuits with classical optimization to solve chemistry and materials science problems on near-term quantum devices. |
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