netket.vqs.VariationalState#

class netket.vqs.VariationalState#

Bases: abc.ABC

Abstract class for variational states representing either pure states or mixed quantum states.

A variational state is a quantum state depending on a set of parameters, and that supports operations such as computing quantum expectation values and their gradients.

A Variational stat can be serialized using flax’s msgpack machinery. See their docs.

Inheritance
Inheritance diagram of netket.vqs.VariationalState
__init__(hilbert)[source]#

Initialize the Abstract base class of a Variational State defined on an hilbert space.

Parameters

hilbert (AbstractHilbert) – The hilbert space upon which this state is defined.

Attributes
hilbert#

The descriptor of the Hilbert space on which this variational state is defined.

Return type

AbstractHilbert

model_state#

The optional pytree with the mutable state of the model.

Return type

Optional[Any]

n_parameters#

The total number of parameters in the model.

Return type

int

parameters#

The pytree of the parameters of the model.

Return type

Any

variables#

The PyTree containing the parameters and state of the model, used when evaluating it.

Return type

Any

Methods
expect(Ô)[source]#
Estimates the quantum expectation value for a given operator O.

In the case of a pure state $psi$, this is $<O>= <Psi|O|Psi>/<Psi|Psi>$ otherwise for a mixed state $rho$, this is $<O> = Tr[rho hat{O}/Tr[rho]$.

Parameters
Return type

Stats

Returns

An estimation of the quantum expectation value <O>.

expect_and_forces(Ô, *, mutable=None)[source]#

Estimates the quantum expectation value and corresponding force vector for a given operator O.

The force vector F_j is defined as the covariance of log-derivative of the trial wave function and the local estimators of the operator. For complex holomorphic states, this is equivalent to the expectation gradient d<O>/d(θ_j)* = F_j. For real-parameter states, the gradient is given by d<O>/dθ_j = 2 Re[F_j].

Parameters
  • – The operator Ô for which expectation value and force are computed.

  • mutable (Union[bool, str, Collection[str], DenyList, None]) – Can be bool, str, or list. Specifies which collections in the model_state should be treated as mutable: bool: all/no collections are mutable. str: The name of a single mutable collection. list: A list of names of mutable collections. This is used to mutate the state of the model while you train it (for example to implement BatchNorm. Consult Flax’s Module.apply documentation for a more in-depth explanation).

  • Ô (netket.operator.AbstractOperator) –

Return type

Tuple[Stats, Any]

Returns

An estimate of the quantum expectation value <O>. An estimate of the forve vector F_j = cov[dlog(ψ)/dx_j, O_loc].

expect_and_grad(Ô, *, mutable=None, use_covariance=None)[source]#

Estimates the quantum expectation value and its gradient for a given operator O.

Parameters
  • – The operator Ô for which expectation value and gradient are computed.

  • mutable (Union[bool, str, Collection[str], DenyList, None]) –

    Can be bool, str, or list. Specifies which collections in the model_state should be treated as mutable: bool: all/no collections are mutable. str: The name of a single mutable collection. list: A list of names of mutable collections. This is used to mutate the state of the model while you train it (for example to implement BatchNorm. Consult Flax’s Module.apply documentation for a more in-depth explanation).

  • use_covariance (Optional[bool]) – whether to use the covariance formula, usually reserved for hermitian operators, ⟨∂logψ Oˡᵒᶜ⟩ - ⟨∂logψ⟩⟨Oˡᵒᶜ⟩

  • Ô (netket.operator.AbstractOperator) –

Return type

Tuple[Stats, Any]

Returns

An estimate of the quantum expectation value <O>. An estimate of the gradient of the quantum expectation value <O>.

grad(Ô, *, use_covariance=None, mutable=None)[source]#

Estimates the gradient of the quantum expectation value of a given operator O.

Parameters
Returns

An estimation of the average gradient of the quantum expectation value <O>.

Return type

array

init_parameters(init_fun=None, *, seed=None)[source]#

Re-initializes all the parameters with the provided initialization function, defaulting to the normal distribution of standard deviation 0.01.

Warning

The init function will not change the dtype of the parameters, which is determined by the model. DO NOT SPECIFY IT INSIDE THE INIT FUNCTION

Parameters
  • init_fun (Optional[Callable[[Any, Sequence[int], Any], Union[ndarray, DeviceArray, Tracer]]]) – a jax initializer such as jax.nn.initializers.normal(). Must be a Callable taking 3 inputs, the jax PRNG key, the shape and the dtype, and outputting an array with the valid dtype and shape. If left unspecified, defaults to jax.nn.initializers.normal(stddev=0.01)

  • seed (Optional[Any]) – Optional seed to be used. The seed is synced across all MPI processes. If unspecified, uses a random seed.

quantum_geometric_tensor(qgt_type)[source]#

Computes an estimate of the quantum geometric tensor G_ij.

This function returns a linear operator that can be used to apply G_ij to a given vector or can be converted to a full matrix.

Parameters

qgt_type – the optional type of the quantum geometric tensor. By default it is automatically selected.

Returns

A linear operator representing the quantum

geometric tensor.

Return type

nk.optimizer.LinearOperator

reset()[source]#

Resets the internal cache of th variational state. Called automatically when the parameters/state is updated.

to_array(normalize=True)[source]#

Returns the dense-vector representation of this state.

Parameters

normalize (bool) – If True, the vector is normalized to have L2-norm 1.

Return type

ndarray

Returns

An exponentially large vector representing the state in the computational basis.

to_qobj()[source]#

Convert the variational state to a qutip’s ket Qobj.

Returns

A qutip.Qobj object.