MetropolisExchangePt

This sampler performs parallel-tempering moves in addition to the local exchange moves implemented in MetropolisExchange. The number of replicas can be $N_{\mathrm{rep}}$ chosen by the user.

Class Constructor

Constructs a new MetropolisExchangePt sampler given a machine, a graph, and a number of replicas.

Argument Type Description
machine netket._C_netket.machine.Machine A machine $\Psi(s)$ used for the sampling. The probability distribution being sampled from is $F(\Psi(s))$, where the function $F(X)$, is arbitrary, by default $F(X)=|X|^2$.
graph netket._C_netket.graph.Graph A graph used to define the distances among the degrees of freedom being sampled.
d_max int=1 The maximum graph distance allowed for exchanges.
n_replicas int=1 The number of replicas used for parallel tempering.

Examples

Sampling from a RBM machine in a 1D lattice of spin 1/2, using nearest-neighbours exchanges.

>>> import netket as nk
>>>
>>> g=nk.graph.Hypercube(length=10,n_dim=2,pbc=True)
>>> hi=nk.hilbert.Spin(s=0.5,graph=g)
>>>
>>> # RBM Spin Machine
>>> ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
>>>
>>> # Construct a MetropolisExchange Sampler with parallel tempering
>>> sa = nk.sampler.MetropolisExchangePt(machine=ma,graph=g,d_max=1,n_replicas=16)



Class Methods

reset

Resets the state of the sampler, including the acceptance rate statistics and optionally initializing at random the visible units being sampled.

Argument Type Description
init_random bool=False If True the quantum numbers (visible units)

seed

Seeds the random number generator used by the Sampler.

Argument Type Description
base_seed int The base seed for the random number generator

sweep

Performs a sampling sweep. Typically a single sweep consists of an extensive number of local moves.

Properties

Property Type Description
acceptance numpy.array The measured acceptance rate for the sampling. In the case of rejection-free sampling this is always equal to 1.
hilbert netket.hilbert The Hilbert space used for the sampling.
machine netket.machine The machine used for the sampling.
machine_func function(complex) The function to be used for sampling. by default $|\Psi(x)|^2$ is sampled, however in general $F(\Psi(v))$
visible numpy.array The quantum numbers being sampled, and distributed according to $F(\Psi(v))$