netket.variational.MCMixedState¶
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class
netket.variational.MCMixedState(sampler, model=None, *, sampler_diag=None, n_samples_diag=1000, n_discard_diag=None, seed=DeviceArray([0, 1042985171], dtype=uint32), sampler_seed=None, variables=None, **kwargs)[source]¶ Bases:
netket.variational.base.VariationalMixedState,netket.variational.mc_state.MCStateVariational State for a Mixed Variational Neural Quantum State.
The state is sampled according to the provided sampler, and it’s diagonal is sampled according to another sampler.
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__init__(sampler, model=None, *, sampler_diag=None, n_samples_diag=1000, n_discard_diag=None, seed=DeviceArray([0, 1042985171], dtype=uint32), sampler_seed=None, variables=None, **kwargs)[source]¶ Constructs the MCMixedState. Arguments are the same as
MCState.- Parameters
- Keyword Arguments
n_samples – the total number of samples across chains and processes when sampling (default=1000).
n_discard – number of discarded samples at the beginning of each monte-carlo chain (default=n_samples/10).
parameters – Optional PyTree of weights from which to start.
seed – rng seed used to generate a set of parameters (only if parameters is not passed). Defaults to a random one.
sampler_seed – rng seed used to initialise the sampler. Defaults to a random one.
mutable – Dict specifing mutable arguments. Use it to specify if the model has a state that can change during evaluation, but that should not be optimised. See also flax.linen.module.apply documentation (default=False)
init_fun – Function of the signature f(model, shape, rng_key, dtype) -> Optional_state, parameters used to initialise the parameters. Defaults to the standard flax initialiser. Only specify if your network has a non-standard init method.
apply_fun – Function of the signature f(model, variables, σ) that should evaluate the model. Defafults to model.apply(variables, σ). specify only if your network has a non-standard apply method.
training_kwargs – a dict containing the optionaal keyword arguments to be passed to the apply_fun during training. Useful for example when you have a batchnorm layer that constructs the average/mean only during training.
- Attributes
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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
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chain_length_diag¶ Length of the markov chain used for sampling the diagonal configurations.
If running under MPI, the total samples will be n_nodes * chain_length * n_batches.
- Return type
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diagonal¶
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hilbert¶ The descriptor of the Hilbert space on which this variational state is defined.
- Return type
AbstractHilbert
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hilbert_physical¶ - Return type
AbstractHilbert
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model_state: Optional[Any]¶ An Optional PyTree encoding a mutable state of the model that is not trained.
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n_discard_diag¶ Number of discarded samples at the beginning of the markov chain used to sample the diagonal of this mixed state.
- Return type
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n_samples_diag¶ The total number of samples generated at every sampling step when sampling the diagonal of this mixed state.
- Return type
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sampler¶ The Monte Carlo sampler used by this Monte Carlo variational state.
- Return type
Sampler
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sampler_diag¶ The Monte Carlo sampler used by this Monte Carlo variational state to sample the diagonal.
- Return type
Sampler
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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
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- Methods
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evaluate(σ)¶ DEPRECATED: use log_value instead.
- Return type
ndarray- Parameters
σ (jax._src.numpy.lax_numpy.ndarray) –
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expect(Ô)¶ - 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
Ô – the operator O.
Ô (netket.operator._abstract_operator.AbstractOperator) –
- Return type
Stats- Returns
An estimation of the quantum expectation value <O>.
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expect_and_grad(Ô, mutable=None, is_hermitian=None)¶ Estimates both the gradient of the quantum expectation value of a given operator O.
- Parameters
Ô – the operator Ô for which we compute the expectation value and it’s gradient
mutable (
Optional[bool]) – 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).is_hermitian (
Optional[bool]) – optional override for whever to use or not the hermitian logic. By default it’s automatically detected.Ô (netket.operator._abstract_operator.AbstractOperator) –
- Return type
- Returns
An estimation of the quantum expectation value <O>. An estimation of the average gradient of the quantum expectation value <O>.
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expect_operator(Ô)[source]¶ - Return type
Stats- Parameters
Ô (netket.operator._abstract_operator.AbstractOperator) –
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grad(Ô, *, is_hermitian=None, mutable=None)¶ Estimates the gradient of the quantum expectation value of a given operator O.
- Parameters
op (netket.operator.AbstractOperator) – the operator O.
is_hermitian (
Optional[bool]) – optional override for whever to use or not the hermitian logic. By default it’s automatically detected.mutable (Optional[Any]) –
- Returns
An estimation of the average gradient of the quantum expectation value <O>.
- Return type
array
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grad_operator(Ô)¶ - Return type
Stats- Parameters
Ô (netket.operator._abstract_operator.AbstractOperator) –
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init(seed=None, dtype=None)¶ Initialises the variational parameters of the variational state.
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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
init_fun (
Optional[Callable[[Any,Sequence[int],Any],Any]]) – a jax initializer such as netket.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 tonetket.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.
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log_value(σ)¶ 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]andσc=σ[::,N:].Given a batch of inputs (Nb, N), returns a batch of outputs (Nb,).
- Return type
ndarray- Parameters
σ (jax._src.numpy.lax_numpy.ndarray) –
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quantum_geometric_tensor(sr)¶ 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.
- Returns
A linear operator representing the quantum geometric tensor.
- Return type
- Parameters
sr (netket.optimizer.sr.api.SR) –
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reset()[source]¶ Resets the internal cache of th variational state. Called automatically when the parameters/state is updated.
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sample(*, chain_length=None, n_samples=None, n_discard=None)¶ 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.
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