Faster-than-the-Clock Quantum Monte Carlo

In this talk we present a way to naturally merge and extend Monte Carlo accelation techniques with the sampling of set functions, which can be viewed as the partition function of a bosonic system. The algorithm is particularly suited for the Connected Determinant Diagrammatic Monte Carlo algorithm (CDet) as well as its extensions and generalizations. Our proposed algorithm is rejection-free and allows for variance and autocorrelation time reduction by making use of the exponential information contained in the full set structure. Finally, we present numerical results for the two-dimensional Hubbard model obtained with this technique.