This sampler acts on all particle positions simultaneously and takes a Langevin step [1]:

$x_{t+dt} = x_t + dt \nabla_x \log p(x) \vert_{x=x_t} + \sqrt{2 dt}\eta,$

where $$\eta$$ is normal distributed noise $$\eta \sim \mathcal{N}(0,1)$$. This sampler only works for continuous Hilbert spaces.

Parameters
• hilbert – The continuous Hilbert space to sample.

• dt – Time step size for the Langevin dynamics (noise with variance 2*dt).

• chunk_size – Chunk size to compute the gradients of the log probability.

• 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).

• n_sweeps – 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