Methoden vergleichen
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| Lattice QCD× | Quantum Monte Carlo× | |
|---|---|---|
| Fachgebiet | Quantencomputing | Quantencomputing |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1974 | 1953 |
| Urheber≠ | Kenneth Wilson | Nicholas Metropolis and colleagues |
| Typ≠ | Simulation method | Monte Carlo simulation |
| Wegweisende Quelle≠ | Wilson, K. G. (1974). Confinement of quarks. Physical Review D, 10, 2445–2459. 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≠ | LQCD, lattice gauge theory | QMC, variational Monte Carlo, diffusion Monte Carlo |
| Verwandt | 3 | 3 |
| Zusammenfassung≠ | Lattice Quantum Chromodynamics (LQCD) is a computational method for studying quantum chromodynamics (QCD)—the theory of strong nuclear forces—by discretizing spacetime onto a lattice and simulating quark and gluon dynamics. Introduced by Kenneth Wilson in 1974, LQCD is the only known approach for non-perturbative calculations of QCD properties from first principles. | 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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