Monte Carlo simulations of quantum field theories on a lattice become increasingly expensive as the continuum limit is approached since the cost per independent sample grows with a high power of the inverse lattice spacing. Simulations on fine lattices suffer from critical slowdown, the rapid growth of autocorrelations in the Markov chain with decreasing lattice spacing a. This causes a strong increase in the number of lattice configurations that have to be generated to obtain statistically significant results. In this paper, hierarchical sampling methods to tame this growth in autocorrelations are discussed. Combined with multilevel variance reduction techniques, this significantly reduces the computational cost of simulations for given tolerances εdisc on the discretisation error and εstat on the statistical error. For an observable with lattice errors of order α and an integrated autocorrelation time that grows like τint ∼ a-z, multilevel Monte Carlo can reduce the cost from O(εstat-2 εdisc-(1+z)/α) to O(εstat-2 |log(εdisc)|2disc-1) or O(εstat-2disc-1). Even higher performance gains are expected for non-perturbative simulations of quantum field theories in D dimensions. The efficiency of the approach is demonstrated on two non-trivial model systems in quantum mechanics, including a topological oscillator that is badly affected by critical slowdown due to freezing of the topological charge. On fine lattices, the new methods are several orders of magnitude faster than standard, single level sampling based on Hybrid Monte Carlo. For high resolutions, multilevel Monte Carlo can be used to accelerate even the cluster algorithm for the topological oscillator. Performance is further improved through perturbative matching. This guarantees efficient coupling of theories on the multilevel lattice hierarchy, which have a natural interpretation in terms of effective theories obtained by renormalisation group transformations.
Original languageEnglish
Number of pages24
JournalPhysical Review D
Publication statusAcceptance date - 18 Nov 2020


  • Multilevel Monte Carlo
  • Path Integral
  • Hierarchical Methods
  • Numerical Algorithm

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