OperatorStateFn
class qiskit.opflow.state_fns.OperatorStateFn(*args, **kwargs)
Bases: StateFn
Deprecated: A class for state functions and measurements which are defined by a density Operator, stored using an OperatorBase.
The class qiskit.opflow.state_fns.operator_state_fn.OperatorStateFn is deprecated as of qiskit-terra 0.24.0. It will be removed in the Qiskit 1.0 release. For code migration guidelines, visit https://qisk.it/opflow_migration.
Parameters
- primitive – The
OperatorBasewhich defines the behavior of the underlying State function. - coeff – A coefficient by which to multiply the state function
- is_measurement – Whether the StateFn is a measurement operator
Attributes
INDENTATION
Default value: ' '
coeff
A coefficient by which the state function is multiplied.
instance_id
Return the unique instance id.
is_measurement
Whether the StateFn object is a measurement Operator.
num_qubits
parameters
primitive
Type: OperatorBase
The primitive which defines the behavior of the underlying State function.
settings
Return settings.
Methods
add
add(other)
Return Operator addition of self and other, overloaded by +.
Parameters
other (OperatorBase) – An OperatorBase with the same number of qubits as self, and in the same ‘Operator’, ‘State function’, or ‘Measurement’ category as self (i.e. the same type of underlying function).
Returns
An OperatorBase equivalent to the sum of self and other.
Return type
adjoint
adjoint()
Return a new Operator equal to the Operator’s adjoint (conjugate transpose), overloaded by ~. For StateFns, this also turns the StateFn into a measurement.
Returns
An OperatorBase equivalent to the adjoint of self.
Return type
eval
eval(front=None)
Evaluate the Operator’s underlying function, either on a binary string or another Operator. A square binary Operator can be defined as a function taking a binary function to another binary function. This method returns the value of that function for a given StateFn or binary string. For example, op.eval('0110').eval('1110') can be seen as querying the Operator’s matrix representation by row 6 and column 14, and will return the complex value at those “indices.” Similarly for a StateFn, op.eval('1011') will return the complex value at row 11 of the vector representation of the StateFn, as all StateFns are defined to be evaluated from Zero implicitly (i.e. it is as if .eval('0000') is already called implicitly to always “indexing” from column 0).
If front is None, the matrix-representation of the operator is returned.
Parameters
front (str |dict |ndarray |OperatorBase |Statevector | None) – The bitstring, dict of bitstrings (with values being coefficients), or StateFn to evaluated by the Operator’s underlying function, or None.
Returns
The output of the Operator’s evaluation function. If self is a StateFn, the result is a float or complex. If self is an Operator (PrimitiveOp, ComposedOp, SummedOp, EvolvedOp, etc.), the result is a StateFn. If front is None, the matrix-representation of the operator is returned, which is a MatrixOp for the operators and a VectorStateFn for state-functions. If either self or front contain proper ListOps (not ListOp subclasses), the result is an n-dimensional list of complex or StateFn results, resulting from the recursive evaluation by each OperatorBase in the ListOps.
Return type
permute
permute(permutation)
Permute the qubits of the state function.
Parameters
permutation (List[int]) – A list defining where each qubit should be permuted. The qubit at index j of the circuit should be permuted to position permutation[j].
Returns
A new StateFn containing the permuted primitive.
Return type
primitive_strings
primitive_strings()
Return a set of strings describing the primitives contained in the Operator. For example, {'QuantumCircuit', 'Pauli'}. For hierarchical Operators, such as ListOps, this can help illuminate the primitives represented in the various recursive levels, and therefore which conversions can be applied.
Returns
A set of strings describing the primitives contained within the Operator.
Return type
sample
sample(shots=1024, massive=False, reverse_endianness=False)
Sample the state function as a normalized probability distribution. Returns dict of bitstrings in order of probability, with values being probability.
Parameters
- shots (int) – The number of samples to take to approximate the State function.
- massive (bool) – Whether to allow large conversions, e.g. creating a matrix representing over 16 qubits.
- reverse_endianness (bool) – Whether to reverse the endianness of the bitstrings in the return dict to match Terra’s big-endianness.
Returns
A dict containing pairs sampled strings from the State function and sampling frequency divided by shots.
tensor
tensor(other)
Return tensor product between self and other, overloaded by ^. Note: You must be conscious of Qiskit’s big-endian bit printing convention. Meaning, Plus.tensor(Zero) produces a |+⟩ on qubit 0 and a |0⟩ on qubit 1, or |+⟩⨂|0⟩, but would produce a QuantumCircuit like
|0⟩– |+⟩–
Because Terra prints circuits and results with qubit 0 at the end of the string or circuit.
Parameters
other (OperatorBase) – The OperatorBase to tensor product with self.
Returns
An OperatorBase equivalent to the tensor product of self and other.
Return type
to_circuit_op
to_circuit_op()
Return StateFnCircuit corresponding to this StateFn. Ignore for now because this is undefined. TODO maybe call to_pauli_op and diagonalize here, but that could be very inefficient, e.g. splitting one Stabilizer measurement into hundreds of 1 qubit Paulis.
to_density_matrix
to_density_matrix(massive=False)
Return numpy matrix of density operator, warn if more than 16 qubits to force the user to set massive=True if they want such a large matrix. Generally big methods like this should require the use of a converter, but in this case a convenience method for quick hacking and access to classical tools is appropriate.
Return type
to_matrix
to_matrix(massive=False)
Note: this does not return a density matrix, it returns a classical matrix containing the quantum or classical vector representing the evaluation of the state function on each binary basis state. Do not assume this is is a normalized quantum or classical probability vector. If we allowed this to return a density matrix, then we would need to change the definition of composition to be ~Op @ StateFn @ Op for those cases, whereas by this methodology we can ensure that composition always means Op @ StateFn.
Return numpy vector of state vector, warn if more than 16 qubits to force the user to set massive=True if they want such a large vector.
Parameters
massive (bool) – Whether to allow large conversions, e.g. creating a matrix representing over 16 qubits.
Returns
Vector of state vector
Return type
np.ndarray
Raises
ValueError – Invalid parameters.