This sampler performs parallel-tempering moves in addition to the local moves implemented in MetropolisHamiltonian. The number of replicas can be chosen by the user.

Class Constructor

Constructs a new MetropolisHamiltonianPt sampler given a machine, a Hamiltonian operator (or in general an arbitrary Operator), and the number of replicas.

Argument Type Description
machine netket._C_netket.machine.Machine A machine used for the sampling. The probability distribution being sampled from is , where the function , is arbitrary, by default .
hamiltonian netket._C_netket.Operator The operator used to perform off-diagonal transition.
n_replicas int The number of replicas used for parallel tempering.


Sampling from a RBM machine in a 1D lattice of spin 1/2

>>> 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)
>>> # Transverse-field Ising Hamiltonian
>>> ha = nk.operator.Ising(hilbert=hi, h=1.0)
>>> # Construct a MetropolisHamiltonianPt Sampler
>>> sa = nk.sampler.MetropolisHamiltonianPt(machine=ma,hamiltonian=ha,n_replicas=10)

Class Methods


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)


Seeds the random number generator used by the Sampler.

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


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


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 is sampled, however in general
visible numpy.array The quantum numbers being sampled, and distributed according to