ProductDual¶

class ProductDual(frames: dict[tuple[int, ...], T])[source]¶

Bases: ProductFrame[MultiQubitDual], BaseDual

Class to represent a set of product Dual operators.

A product Dual \(D\) is made of local Duals \(D1, D2, ...\) acting on respective subsystems. Each global effect can be written as the tensor product of local effects, \(D_{k_1, k_2, ...} = D1_{k_1} \otimes D2_{k__2} \otimes \cdots\).

Initialize from a mapping of local frames.

Parameters:

frames (dict[tuple[int, ...], T]) – a dictionary mapping from a tuple of subsystem indices to a local frame objects.

Raises:

ValueError – if any key in frames is not a tuple consisting of unique integers. In other words, every local frame must act on a distinct set of subsystem indices which do not overlap with each other.
ValueError – if any key in frames re-uses a previously used subsystem index. In other words, all local frames must act on mutually exclusive subsystem indices.
ValueError – if any key in frames does not specify the number of subsystem indices, which matches the number of systems acted upon by that local frame (MultiQubitFrame.num_subsystems()).

Inherited Attributes

dimension¶: The dimension of the Hilbert space on which the effects act.

informationally_complete¶: If the frame spans the entire Hilbert space.

num_operators¶: The number of effects of the frame.

num_outcomes¶: The number of outcomes of the Dual.

num_subsystems¶

The number of subsystems which the frame operators act on.

For qubits, this is always \(\log_2(\)dimension\()\).

shape¶: Give the number of operators per sub-system.

sub_systems¶: Give the number of operators per sub-system.

Methods

classmethod build_dual_from_frame(frame: BaseFrame, alphas: tuple[tuple[float, ...] | None, ...] | None = None) → ProductDual[source]¶

Construct a dual frame to another (primal) frame.

Parameters:

frame (BaseFrame) – The primal frame from which we will build the dual frame.
alphas (tuple[tuple[float, ...] | None, ...] | None) – parameters of the frame super-operator used to build the dual frame. If None, the parameters are set as the traces of each operator in the primal frame.

Returns:

A dual frame to the supplied frame.

Return type:

ProductDual

is_dual_to(frame: BaseFrame) → bool[source]¶

Check if self is a dual to another frame.

Parameters:: frame (BaseFrame) – the other frame to check duality against.
Returns:: Whether self is dual to frame.
Return type:: bool

Inherited Methods

Return the frame coefficients of hermitian_op.

This method implements the analysis operator \(A\) of the frame \(\{F_k\}_k\):

\[A: \mathcal{O} \mapsto \{ \mathrm{Tr}\left[F_k \mathcal{O} \right] \}_k,\]

where \(c_k = \mathrm{Tr}\left[F_k \mathcal{O} \right]\) are called the frame coefficients of the Hermitian operator \(\mathcal{O}\).

Parameters:

hermitian_op (SparsePauliOp | Operator) – a hermitian operator whose frame coefficients to compute.
frame_op_idx (tuple[int, ...] | set[tuple[int, ...]] | None) – label or set of labels indicating which coefficients are queried. If None, all coefficients are queried.

Returns:

Frame coefficients, specified by frame_op_idx, of the Hermitian operator hermitian_op. If a specific coefficient was queried, a float is returned. If a specific set of coefficients was queried, a dictionary mapping labels to coefficients is returned. If all coefficients were queried, an array with all coefficients is returned.

Raises:

TypeError – when the provided single or sequence of labels frame_op_idx does not have a valid type.
ValueError – when the dimension of the provided hermitian_op does not match the dimension of the frame operators.

Return type:

float | dict[tuple[int, …], float] | ndarray

classmethod from_list(frames: Sequence[T]) → Self¶

Construct a ProductFrame from a list of MultiQubitFrame objects.

This is a convenience method to simplify the construction of a ProductFrame for the cases in which the local frame objects act on a sequential order of subsystems. In other words, this method converts the sequence of frames to a dictionary of frames in accordance with the input to ProductFrame.__init__() by using the positions along the sequence as subsystem indices.

Below are some examples:

>>> from qiskit.quantum_info import Operator
>>> from povm_toolbox.quantum_info import SingleQubitPOVM, MultiQubitPOVM, ProductPOVM

>>> sqp = SingleQubitPOVM([Operator.from_label("0"), Operator.from_label("1")])
>>> product = ProductPOVM.from_list([sqp, sqp])
>>> # is equivalent to
>>> product = ProductPOVM({(0,): sqp, (1,): sqp})

>>> mqp = MultiQubitPOVM(
...     [
...         Operator.from_label("00"),
...         Operator.from_label("01"),
...         Operator.from_label("10"),
...         Operator.from_label("11"),
...     ]
... )
>>> product = ProductPOVM.from_list([mqp, mqp])
>>> # is equivalent to
>>> product = ProductPOVM({(0, 1): mqp, (2, 3): mqp})

>>> product = ProductPOVM.from_list([sqp, sqp, mqp])
>>> # is equivalent to
>>> product = ProductPOVM({(0,): sqp, (1,): sqp, (2, 3): mqp})

>>> product = ProductPOVM.from_list([sqp, mqp, sqp])
>>> # is equivalent to
>>> product = ProductPOVM({(0,): sqp, (1, 2): mqp, (3,): sqp})

Parameters:: frames (Sequence[T]) – a sequence of MultiQubitFrame objects.
Returns:: A new ProductFrame instance.
Return type:: Self

Return the decomposition weights of the provided observable.

Computes the \(\omega_k\) in

\[\mathcal{O} = \sum_{k=1}^n \omega_k M_k\]

where \(\mathcal{O}\) is the observable and \(M_k\) are the effects of the POVM of which self is the dual. The closed form for computing \(\omega_k\) is

\[\omega_k = \text{Tr}\left[\mathcal{O} D_k\right]\]

where \(D_k\) make of this dual frame (i.e. self).

Note

In the frame theory formalism, the mapping \(A: \mathcal{O} \mapsto \{\text{Tr}\left[\mathcal{O} D_k\right]\}_k\) is referred to as the analysis operator, which is implemented by the analysis() method.

Parameters:

observable (SparsePauliOp | Operator) – the observable for which to compute the decomposition weights.
outcome_idx (LabelT | set[LabelT] | None) – label or set of labels indicating which decomposition weights are queried. If None, all weights are queried.

Returns:

Decomposition weight(s) associated to the effect(s) specified by outcome_idx. If a specific outcome was queried, a float is returned. If a specific set of outcomes was queried, a dictionary mapping outcome labels to weights is returned. If all outcomes were queried, an array with all weights is returned.

Return type:

float | dict[LabelT, float] | ndarray