netket.sampler.MetropolisLocal

netket.sampler.MetropolisLocal(hilbert, **kwargs)

Sampler acting on one local degree of freedom.

This sampler acts locally only on one local degree of freedom \(s_i\), and proposes a new state: \(s_1 \dots s^\prime_i \dots s_N\), where \(s^\prime_i \neq s_i\).

The transition probability associated to this sampler can be decomposed into two steps:

1. One of the site indices \(i = 1\dots N\) is chosen with uniform probability.

2. Among all the possible (\(m - 1\)) values that \(s^\prime_i\) can take, one of them is chosen with uniform probability.

For example, in the case of spin \(1/2\) particles, \(m=2\) and the possible local values are \(s_i = -1,+1\). In this case then MetropolisLocal is equivalent to flipping a random spin.

In the case of bosons, with occupation numbers \(s_i = 0, 1, \dots n_{\mathrm{max}}\), MetropolisLocal would pick a random local occupation number uniformly between \(0\) and \(n_{\mathrm{max}}\) except the current \(s_i\).

Parameters:

• hilbert – The Hilbert space to sample.

• n_chains – The total number of independent Markov chains across all MPI ranks. Either specify this or n_chains_per_rank.

• n_chains_per_rank – Number of independent chains on every MPI rank (default = 16).

• sweep_size – Number of sweeps for each step along the chain. Defaults to the number of sites in the Hilbert space. This is equivalent to subsampling the Markov chain.

• reset_chains – If True, resets the chain state when reset is called on every new sampling (default = False).

• machine_pow – The power to which the machine should be exponentiated to generate the pdf (default = 2).

• dtype – The dtype of the states sampled (default = np.float64).

Return type:

MetropolisSampler