netket.hilbert.TensorHilbert
netket.hilbert.TensorHilbert#
- class netket.hilbert.TensorHilbert#
Bases:
netket.hilbert.DiscreteHilbertTensor product of several Discrete sub-spaces, representing the space
In general you should not construct this object directly, but you should simply multiply different hilbert spaces together. In this case, Python’s * operator will be interpreted as a tensor product.
This Hilbert can be used as a replacement anywhere a Uniform Hilbert space is not required.
Examples
Couple a bosonic mode with spins
>>> from netket.hilbert import Spin, Fock >>> Fock(3)*Spin(0.5, 5) Fock(n_max=3, N=1)⊗Spin(s=1/2, N=5) >>> type(_) <class 'netket.hilbert.tensor_hilbert.TensorHilbert'>
- Inheritance
- __init__(*hilb_spaces)[source]#
Constructs a tensor Hilbert space
- Parameters
*hilb – An iterable object containing at least 1 hilbert space.
hilb_spaces (netket.hilbert.DiscreteHilbert) –
- Attributes
- is_finite#
- Methods
- all_states(out=None)#
Returns all valid states of the Hilbert space.
Throws an exception if the space is not indexable.
- numbers_to_states(numbers, out=None)#
Returns the quantum numbers corresponding to the n-th basis state for input n. n is an array of integer indices such that
numbers[k]=Index(states[k]). Throws an exception iff the space is not indexable.
- ptrace(sites)[source]#
Returns the hilbert space without the selected sites.
Not all hilbert spaces support this operation.
- 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
- 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.]]
- size_at_index(i)#
Size of the local degrees of freedom for the i-th variable.
- states()#
Returns an iterator over all valid configurations of the Hilbert space. Throws an exception iff the space is not indexable. Iterating over all states with this method is typically inefficient, and
`all_states`should be preferred.
- states_at_index(i)[source]#
A list of discrete local quantum numbers at the site i. If the local states are infinitely many, None is returned.
- Parameters
i – The index of the desired site.
- Returns
A list of values or None if there are infinitely many.
- states_to_local_indices(x)[source]#
Returns a tensor with the same shape of x, where all local values are converted to indices in the range 0…self.shape[i]. This function is guaranteed to be jax-jittable.
For the Fock space this returns x, but for other hilbert spaces such as Spin this returns an array of indices.
NOTE: This function is experimental. Use at your own risk.
- Parameters
x – a tensor containing samples from this hilbert space
- Returns
a tensor containing integer indices into the local hilbert
- states_to_numbers(states, out=None)#
Returns the basis state number corresponding to given quantum states. The states are given in a batch, such that states[k] has shape (hilbert.size). Throws an exception iff the space is not indexable.