# netket.hilbert.Particle#

class netket.hilbert.Particle#

Hilbert space derived from AbstractParticle for Fermions.

Inheritance
__init__(N, L, pbc)[source]#

Constructs new Particles given specifications of the continuous space they are defined in.

Parameters
• N (int) – Number of particles

• L (Tuple[float, ...]) – spatial extension in each spatial dimension

• pbc (Union[bool, Tuple[bool, ...]]) – Whether or not to use periodic boundary conditions for each spatial dimension. If bool, its value will be used for all spatial dimensions.

Attributes
extent#

Spatial extension in each spatial dimension

Return type
is_indexable#

Whever the space can be indexed with an integer

Return type

bool

n_particles#

The number of particles

Return type

int

pbc#

Whether or not to use periodic boundary conditions for each spatial dimension

Return type

Tuple[bool, ...]

size#
Return type

int

Methods
ptrace(sites)#

Returns the hilbert space without the selected sites.

Not all hilbert spaces support this operation.

Parameters

sites (Union[int, Iterable]) – a site or list of sites to trace away

Return type

AbstractHilbert

Returns

The partially-traced hilbert space. The type of the resulting hilbert space might be different from the starting one.

random_state(key=None, size=None, dtype=<class 'numpy.float32'>)#

Generates either a single or a batch of uniformly distributed random states. Runs as random_state(self, key, size=None, dtype=np.float32) by default.

Parameters
• key – rng state from a jax-style functional generator.

• size (Optional[int]) – If provided, returns a batch of configurations of the form (size, N) if size is an integer or (*size, N) if it is a tuple and where $$N$$ is the Hilbert space size. By default, a single random configuration with shape (#,) is returned.

• dtype – DType of the resulting vector.

Return type

ndarray

Returns

A state or batch of states sampled from the uniform distribution on the hilbert space.

Example

>>> import netket, jax
>>> hi = netket.hilbert.Qubit(N=2)
>>> k1, k2 = jax.random.split(jax.random.PRNGKey(1))
>>> print(hi.random_state(key=k1))
[1. 0.]
>>> print(hi.random_state(key=k2, size=2))
[[0. 0.]
[0. 1.]]