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| Quanten-Approximations-Optimierungsalgorithmus× | Quantum Monte Carlo× | |
|---|---|---|
| Fachgebiet | Quantencomputing | Quantencomputing |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2014 | 1953 |
| Urheber≠ | Edward Farhi | Nicholas Metropolis and colleagues |
| Typ≠ | Hybrid quantum-classical algorithm | Monte Carlo simulation |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen≠ | QAOA, quantum alternating operator ansatz | QMC, variational Monte Carlo, diffusion Monte Carlo |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | 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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