Observables

Observable utilities for states and density matrices.

openquantumsim.observables.expect(operator, state)

Expectation value of an operator for a ket or density matrix.

Parameters:

• operator (Operator)

• state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

complex

openquantumsim.observables.von_neumann_entropy(rho, *, base=2.0)

Von Neumann entropy -Tr(rho log rho).

Parameters:

• rho (ndarray[tuple[Any, ...], dtype[complex128]])

• base (float)

Return type:

float

openquantumsim.observables.renyi_entropy(state, alpha=2.0, *, base=2.0)

Renyi entropy S_alpha = log(Tr(rho^alpha))/(1-alpha).

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• alpha (float)

• base (float)

Return type:

float

openquantumsim.observables.purity(state, *, normalize=False)

Return Tr(rho^2) for a ket or density matrix.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• normalize (bool)

Return type:

float

openquantumsim.observables.linear_entropy(state, *, normalized=False)

Linear entropy 1 - Tr(rho^2).

Set normalized=True to scale by d/(d-1) for a d-dimensional state, giving 1 for a maximally mixed state.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• normalized (bool)

Return type:

float

openquantumsim.observables.participation_ratio(state)

Effective number of populated eigenstates, 1 / Tr(rho^2).

Parameters:

state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.fidelity(state_a, state_b, *, squared=True)

Uhlmann state fidelity between two kets or density matrices.

By default this returns the squared convention F = (Tr sqrt(sqrt(rho) sigma sqrt(rho)))^2. For pure states this is |<psi|phi>|^2; for rho and |psi> it is <psi|rho|psi>. Set squared=False to return the root fidelity.

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

• squared (bool)

Return type:

float

openquantumsim.observables.infidelity(state_a, state_b)

Return 1 - fidelity(state_a, state_b).

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.trace_norm(matrix, *, hermitian=None)

Trace norm Tr(sqrt(A†A)).

Parameters:

• matrix (ndarray[tuple[Any, ...], dtype[complex128]])

• hermitian (bool | None)

Return type:

float

openquantumsim.observables.trace_distance(state_a, state_b)

Trace distance 0.5 * ||rho - sigma||_1.

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.hilbert_schmidt_distance(state_a, state_b)

Hilbert-Schmidt distance sqrt(Tr((rho-sigma)^2)).

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.bures_angle(state_a, state_b)

Bures angle acos(sqrt(F)) in radians.

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.bures_distance(state_a, state_b)

Bures distance sqrt(2 * (1 - sqrt(F))).

Parameters:

• state_a (ndarray[tuple[Any, ...], dtype[complex128]])

• state_b (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.is_hermitian(matrix, *, atol=1e-10)

Return whether matrix is Hermitian within atol.

Parameters:

• matrix (ndarray[tuple[Any, ...], dtype[complex128]])

• atol (float)

Return type:

bool

openquantumsim.observables.is_density_matrix(matrix, *, atol=1e-10)

Return whether matrix is positive, Hermitian, and trace one.

Parameters:

• matrix (ndarray[tuple[Any, ...], dtype[complex128]])

• atol (float)

Return type:

bool

openquantumsim.observables.normalize_state(state)

Normalize a ket by norm or a density matrix by trace.

Parameters:

state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

ndarray[tuple[Any, …], dtype[complex128]]

openquantumsim.observables.populations(state)

Return basis populations from a ket or density matrix.

Parameters:

state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

ndarray[tuple[Any, …], dtype[float64]]

openquantumsim.observables.l1_coherence(state)

Basis-dependent l1 coherence, sum of off-diagonal magnitudes.

Parameters:

state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

float

openquantumsim.observables.bloch_vector(state)

Return the Bloch vector (<sigma_x>, <sigma_y>, <sigma_z>).

Parameters:

state (ndarray[tuple[Any, ...], dtype[complex128]])

Return type:

ndarray[tuple[Any, …], dtype[float64]]

openquantumsim.observables.evaluate_state_observables(states, observables)

Evaluate named scalar callbacks on each saved ket or density matrix.

Parameters:

• states (Sequence[ndarray[tuple[Any, ...], dtype[complex128]]])

• observables (Mapping[str, object])

Return type:

dict[str, ndarray[tuple[Any, …], dtype[complex128]]]

openquantumsim.observables.state_metrics(*, purity=False, entropy=False, linear_entropy=False, participation_ratio=False, population_indices=None, l1_coherence=False, bloch_vector=False, fidelity_to=None, trace_distance_to=None)

Build common state-observable callbacks for solver runs.

The returned mapping can be passed directly to solvers via state_observables=.... mcsolve aggregates built-in metrics in the backend when they reduce to linear expectations or pure-trajectory constants.

Parameters:

• purity (bool)

• entropy (bool)

• linear_entropy (bool)

• participation_ratio (bool)

• population_indices (Sequence[int] | None)

• l1_coherence (bool)

• bloch_vector (bool)

• fidelity_to (ndarray[tuple[Any, ...], dtype[complex128]] | None)

• trace_distance_to (ndarray[tuple[Any, ...], dtype[complex128]] | None)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.fidelity_observable(reference, *, name='fidelity')

Return a named fidelity callback mapping for solver runs.

Parameters:

• reference (ndarray[tuple[Any, ...], dtype[complex128]])

• name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.purity_observable(*, name='purity')

Return a named purity callback mapping for solver runs.

Parameters:

name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.entropy_observable(*, name='entropy')

Return a named von Neumann entropy callback mapping for solver runs.

Parameters:

name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.linear_entropy_observable(*, normalized=False, name='linear_entropy')

Return a named linear-entropy callback mapping for solver runs.

Parameters:

• normalized (bool)

• name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.participation_ratio_observable(*, name='participation_ratio')

Return a named participation-ratio callback mapping for solver runs.

Parameters:

name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.l1_coherence_observable(*, name='l1_coherence')

Return a named l1-coherence callback mapping for solver runs.

Parameters:

name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.trace_distance_observable(reference, *, name='trace_distance')

Return a named trace-distance callback mapping for solver runs.

Parameters:

• reference (ndarray[tuple[Any, ...], dtype[complex128]])

• name (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.population_observable(index, *, name=None)

Return a named callback for one basis population.

Parameters:

• index (int)

• name (str | None)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.population_observables(indices, *, prefix='population')

Return callbacks for selected basis populations.

Parameters:

• indices (Sequence[int])

• prefix (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.bloch_observables(prefix='bloch')

Return callbacks for Bloch-vector components.

Parameters:

prefix (str)

Return type:

dict[str, Callable[[ndarray[tuple[Any, …], dtype[complex128]]], Any]]

openquantumsim.observables.partial_trace(state, dims, keep)

Trace out all subsystems except keep for a ket or density matrix.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• dims (Sequence[int])

• keep (int | Sequence[int])

Return type:

ndarray[tuple[Any, …], dtype[complex128]]

openquantumsim.observables.mutual_information(state, dims, subsystem_a, subsystem_b, *, base=2.0)

Quantum mutual information I(A:B) for arbitrary subsystem groups.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• dims (Sequence[int])

• subsystem_a (int | Sequence[int])

• subsystem_b (int | Sequence[int])

• base (float)

Return type:

float

openquantumsim.observables.bipartite_mutual_information(state, dim_a, dim_b, *, base=2.0)

Quantum mutual information for a two-part system A ⊗ B.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• dim_a (int)

• dim_b (int)

• base (float)

Return type:

float

openquantumsim.observables.partial_traces(state, dim_a, dim_b)

Bipartite reduced density matrices for a ket or density matrix.

Parameters:

• state (ndarray[tuple[Any, ...], dtype[complex128]])

• dim_a (int)

• dim_b (int)

Return type:

tuple[ndarray[tuple[Any, …], dtype[complex128]], ndarray[tuple[Any, …], dtype[complex128]]]