A machine-learned fixed-point action for 4D SU(3) gauge theory is simulated with HMC and shows greatly reduced lattice artifacts in gradient-flow scale setting.
Testing and tuning symplectic integrators for Hybrid Monte Carlo algorithm in lattice QCD
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abstract
We examine a new 2nd order integrator recently found by Omelyan et al. The integration error of the new integrator measured in the root mean square of the energy difference, $\bra\Delta H^2\ket^{1/2}$, is about 10 times smaller than that of the standard 2nd order leapfrog (2LF) integrator. As a result, the step size of the new integrator can be made about three times larger. Taking into account a factor 2 increase in cost, the new integrator is about 50% more efficient than the 2LF integrator. Integrating over positions first, then momenta, is slightly more advantageous than the reverse. Further parameter tuning is possible. We find that the optimal parameter for the new integrator is slightly different from the value obtained by Omelyan et al., and depends on the simulation parameters. This integrator could also be advantageous for the Trotter-Suzuki decomposition in Quantum Monte Carlo.
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HMC and gradient flow with machine-learned classically perfect fixed-point actions
A machine-learned fixed-point action for 4D SU(3) gauge theory is simulated with HMC and shows greatly reduced lattice artifacts in gradient-flow scale setting.