# Fast Fermion Monte Carlo

## TL;DR

This paper explores methods to accelerate the Hybrid Monte Carlo algorithm, finding that adaptive step-size offers no benefit, while using an approximate Hamiltonian or preconditioning significantly improves performance.

## Contribution

It introduces and evaluates two effective strategies—approximate Hamiltonian and preconditioning—for speeding up Hybrid Monte Carlo simulations.

## Key findings

- Adaptive step-size does not improve efficiency.
- Approximate Hamiltonian enhances speed.
- Preconditioning significantly reduces computation time.

## Abstract

Three possibilities to speed up the Hybrid Monte Carlo algorithm are investigated. Changing the step-size adaptively brings no practical gain. On the other hand, substantial improvements result from using an approximate Hamiltonian or a preconditioned action.