neuralqx.vqs.mc package

check_hilbert(A, B)
get_local_kernel_arguments(vstate, Ô)

Returns the samples of vstate used to compute the expectation value of the operator O, and the connected elements and matrix elements.

Parameters:

  • vstate (Any) – the variational state

  • Ô – the operator

Returns:

A Tuple with 2 elements (sigma, args), where the first elements should be the samples over which the classical expectation value should be computed, while the latter is anything that can be fed as input to the local_kernel.

get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator)
get_local_kernel_arguments(vs: netket.vqs.mc.mc_state.state.MCState, O: netket.operator._sum.operator.SumGenericOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator.DiscreteOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._abstract_super_operator.AbstractSuperOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._lazy.Squared[netket.operator._abstract_super_operator.AbstractSuperOperator])
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.PenaltyCost)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator | neuralqx.operators.types._discrete_operator.DiscreteOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.experimental.observable.variance.variance_operator.VarianceObservable)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.base.ComputationalOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.jax.ComputationalJaxOperator)
get_local_kernel_arguments(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.InverseExpectationCost)

Build the args for the InverseExpectationCost with batch-consistent (σ, inner_args)

We will:

  • collapse σ like _expect() does

  • build inner_args from that collapsed σ

  • compute <V> from the same pair

  • pack dynamic scalars (fprime, g)

  • return the σ_collapsed so later kernels see the same layout and avoid recompilation

get_local_kernel(vstate, Ô)

Returns the function computing the local estimator for the given variational state and operator.

Parameters:

  • vstate (Any) – the variational state

  • Ô – the operator

Returns:

A callable accepting the output of get_configs(vstate, O).

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator)
get_local_kernel(vs: netket.vqs.mc.mc_state.state.MCState, O: netket.operator._sum.operator.SumGenericOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared, chunk_size: int)

# Dispatches to select what expect-kernel to use

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._abstract_super_operator.AbstractSuperOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator.DiscreteOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._lazy.Squared[netket.operator._abstract_super_operator.AbstractSuperOperator])
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._abstract_super_operator.AbstractSuperOperator, chunk_size: int)

# Dispatches to select what expect-kernel to use

get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._lazy.Squared[netket.operator._abstract_super_operator.AbstractSuperOperator], chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator.DiscreteOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_mixed_state.state.MCMixedState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.PenaltyCost)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator | neuralqx.operators.types._discrete_operator.DiscreteOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.experimental.observable.variance.variance_operator.VarianceObservable)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.base.ComputationalOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.jax.ComputationalJaxOperator)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.InverseExpectationCost)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator.DiscreteOperator | neuralqx.operators.types._discrete_operator.DiscreteOperator, chunk_size: int)

# standard numba operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._discrete_operator_jax.DiscreteJaxOperator, chunk_size: int)

# standard JAX operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._continuous_operator.ContinuousOperator, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.operator._lazy.Squared, chunk_size: int)

# Squared operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.base.ComputationalOperator, chunk_size: int)

# standard computational operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators.types.computational_operator.jax.ComputationalJaxOperator, chunk_size: int)

# standard computational JAX operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.PenaltyCost, chunk_size: int)

# standard PenaltyCost operators

get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: neuralqx.operators._lazy.InverseExpectationCost, chunk_size: int)
get_local_kernel(vstate: netket.vqs.mc.mc_state.state.MCState | neuralqx.vqs.mc.mc_state.state.MCState, Ô: netket.experimental.observable.variance.variance_operator.VarianceObservable, chunk_size: int)

# VarianceObservable not supported for now

class MCState(sampler, model=None, *, n_samples=None, n_samples_per_rank=None, n_discard_per_chain=None, chunk_size=None, variables=None, init_fun=None, apply_fun=None, seed=None, sampler_seed=None, mutable=False, training_kwargs={}, is_group_averaged=False)

Bases: VariationalState

Variational State for a Variational Neural Quantum State.

The state is sampled according to the provided sampler.

Subpackages

Submodules