The order of updates, in Monte Carlo computations of quantum fields, has been found to have an impact on the accuracy of the correlation function - this impact is dependent on the temperature given to the system. The model analysed was the Ising Model. It was assessed in one, two, and three dimensions. New states of the system were produced using the Markov Chain Monte Carlo method and the Metropolis algorithm. The states were divided into bins and averaged over them to reduce the dependence between consecutive states in the chain. The mean correlation function and its standard deviation over the chain were computed. The order of updates was defined by, inter alia, the Hilbert and Lebesgue space-filling curves.
Project developed as part of the Trinity College Dublin Hamilton Summer Internship 2019.
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