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| Thuật toán Tối ưu hóa Xấp xỉ Lượng tử× | Monte Carlo lượng tử× | |
|---|---|---|
| Lĩnh vực | Tính toán lượng tử | Tính toán lượng tử |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2014 | 1953 |
| Người khởi xướng≠ | Edward Farhi | Nicholas Metropolis and colleagues |
| Loại≠ | Hybrid quantum-classical algorithm | Monte Carlo simulation |
| Công trình gốc≠ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ | Metropolis, N., Rosenbluth, A. W., et al. (1953). Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092. DOI ↗ |
| Tên gọi khác≠ | QAOA, quantum alternating operator ansatz | QMC, variational Monte Carlo, diffusion Monte Carlo |
| Liên quan≠ | 4 | 3 |
| Tóm tắt≠ | 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 Monte Carlo (QMC) is a stochastic computational method for computing ground state properties of quantum many-body systems. Combining classical Monte Carlo sampling with quantum mechanics, QMC approaches are among the most accurate methods available for electronic structure and condensed matter physics, achieving sub-percent accuracy for many systems. |
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