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| 量子支持向量机× | 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 | 2014 | 2014 |
| 提出者≠ | Patrick Rebentrost, Masoud Mohseni, and Seth Lloyd | Edward Farhi |
| 类型≠ | Machine learning algorithm | Hybrid quantum-classical algorithm |
| 开创性文献≠ | Rebentrost, P., Mohseni, M., Lloyd, S. (2014). Quantum support vector machine for big data classification. Physical Review Letters, 113, 130503. DOI ↗ | Farhi, E., Goldstone, J., Gutmann, S. (2014). A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028. DOI ↗ |
| 别名 | QSVM, quantum kernel | QAOA, quantum alternating operator ansatz |
| 相关≠ | 2 | 4 |
| 摘要≠ | Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm combining quantum feature spaces with classical SVM training. Proposed by Rebentrost et al. in 2014, QSVM leverages quantum processors to compute kernel functions, potentially offering speedup for classification problems while remaining practical on near-term quantum devices. | 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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