# netket.vqs.MCState#

class netket.vqs.MCState#

Variational State for a Variational Neural Quantum State.

The state is sampled according to the provided sampler.

Inheritance
__init__(sampler, model=None, *, n_samples=None, n_samples_per_rank=None, n_discard=None, n_discard_per_chain=None, chunk_size=None, variables=None, init_fun=None, apply_fun=None, sample_fun=None, seed=None, sampler_seed=None, mutable=False, training_kwargs={})[source]#

Constructs the MCState.

Parameters
Attributes
chain_length#

Length of the markov chain used for sampling configurations.

If running under MPI, the total samples will be n_nodes * chain_length * n_batches.

Return type

int

chunk_size#

Suggested maximum size of the chunks used in forward and backward evaluations of the Neural Network model. If your inputs are smaller than the chunk size this setting is ignored.

This can be used to lower the memory required to run a computation with a very high number of samples or on a very large lattice. Notice that inputs and outputs must still fit in memory, but the intermediate computations will now require less memory.

This option comes at an increased computational cost. While this cost should be negligible for large-enough chunk sizes, don’t use it unless you are memory bound!

This option is an hint: only some operations support chunking. If you perform an operation that is not implemented with chunking support, it will fall back to no chunking. To check if this happened, set the environment variable NETKET_DEBUG=1.

Return type

int

hilbert#

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

Return type

AbstractHilbert

model#

Returns the model definition of this variational state.

This field is optional, and is set to None if the variational state has been initialized using a custom function.

Return type
model_state: Optional[Any]#

The optional pytree with the mutable state of the model.

Return type
n_discard#

Use n_discard_per_chain instead.

Number of discarded samples at the beginning of the markov chain.

Type

DEPRECATED

Return type

int

n_discard_per_chain#

Number of discarded samples at the beginning of the markov chain.

Return type

int

n_parameters#

The total number of parameters in the model.

Return type

int

n_samples#

The total number of samples generated at every sampling step.

Return type

int

n_samples_per_rank#

The number of samples generated on one MPI rank at every sampling step.

Return type

int

parameters#

The pytree of the parameters of the model.

Return type

Any

sampler#

The Monte Carlo sampler used by this Monte Carlo variational state.

Return type

Sampler

samples#

Returns the set of cached samples.

The samples returnede are guaranteed valid for the current state of the variational state. If no cached parameters are available, then they are sampled first and then cached.

To obtain a new set of samples either use reset or sample.

Return type

ndarray

variables#

The PyTreee containing the paramters and state of the model, used when evaluating it.

Return type

Any

sampler_state: netket.sampler.SamplerState#
Methods
evaluate(σ)[source]#

DEPRECATED: use log_value instead.

Return type

ndarray

Parameters
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
• – the operator O.

Return type

Stats

Returns

An estimation of the quantum expectation value <O>.

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

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

Parameters
• – the operator Ô for which we compute the expectation value and it’s gradient

• mutable (Optional[Any]) – 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 exaplanation).

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

Return type

Tuple[Stats, Any]

Returns

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

grad(Ô, *, use_covariance=None, mutable=None)#

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(seed=None, dtype=None)[source]#

Initialises the variational parameters of the variational state.

init_parameters(init_fun=None, *, seed=None)#

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
local_estimators(op, *, chunk_size=None)[source]#

Compute the local estimators for the operator op (also known as local energies when op is the Hamiltonian) at the current configuration samples self.samples.

$O_\mathrm{loc}(s) = \frac{\langle s | \mathtt{op} | \psi \rangle}{\langle s | \psi \rangle}$

Warning

The samples differ between MPI processes, so returned the local estimators will also take different values on each process. To compute sample averages and similar quantities, you will need to perform explicit operations over all MPI ranks. (Use functions like self.expect to get process-independent quantities without manual reductions.)

Parameters
log_value(σ)[source]#

Evaluate the variational state for a batch of states and returns the logarithm of the amplitude of the quantum state. For pure states, this is $$log(<σ|ψ>)$$, whereas for mixed states this is $$log(<σr|ρ|σc>)$$, where ψ and ρ are respectively a pure state (wavefunction) and a mixed state (density matrix). For the density matrix, the left and right-acting states (row and column) are obtained as σr=σ[::,0:N] and σc=σ[::,N:].

Given a batch of inputs (Nb, N), returns a batch of outputs (Nb,).

Return type

ndarray

Parameters
quantum_geometric_tensor(qgt_T=QGTAuto())[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_T (LinearOperator) – the optional type of the quantum geometric tensor. By default it’s automatically selected.

Returns

A linear operator representing the quantum geometric tensor.

Return type

nk.optimizer.LinearOperator

reset()[source]#

Resets the sampled states. This method is called automatically every time that the parameters/state is updated.

sample(*, chain_length=None, n_samples=None, n_discard_per_chain=None)[source]#

Sample a certain number of configurations.

If one among chain_leength or n_samples is defined, that number of samples are gen erated. Otherwise the value set internally is used.

Parameters
Return type

ndarray

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()#

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

Returns

A qutip.Qobj object.