ZFeatureMap
class qiskit.circuit.library.ZFeatureMap(feature_dimension, reps=2, data_map_func=None, parameter_prefix='x', insert_barriers=False, name='ZFeatureMap')
Bases: PauliFeatureMap
The first order Pauli Z-evolution circuit.
On 3 qubits and with 2 repetitions the circuit is represented by:
┌───┐┌─────────────┐┌───┐┌─────────────┐
┤ H ├┤ P(2.0*x[0]) ├┤ H ├┤ P(2.0*x[0]) ├
├───┤├─────────────┤├───┤├─────────────┤
┤ H ├┤ P(2.0*x[1]) ├┤ H ├┤ P(2.0*x[1]) ├
├───┤├─────────────┤├───┤├─────────────┤
┤ H ├┤ P(2.0*x[2]) ├┤ H ├┤ P(2.0*x[2]) ├
└───┘└─────────────┘└───┘└─────────────┘This is a sub-class of PauliFeatureMap where the Pauli strings are fixed as [‘Z’]. As a result the first order expansion will be a circuit without entangling gates.
Examples
>>> from qiskit.circuit.library import ZFeatureMap
>>> prep = ZFeatureMap(3, reps=3, insert_barriers=True)
>>> print(prep.decompose())
┌───┐ ░ ┌─────────────┐ ░ ┌───┐ ░ ┌─────────────┐ ░ ┌───┐ ░ ┌─────────────┐
q_0: ┤ H ├─░─┤ P(2.0*x[0]) ├─░─┤ H ├─░─┤ P(2.0*x[0]) ├─░─┤ H ├─░─┤ P(2.0*x[0]) ├
├───┤ ░ ├─────────────┤ ░ ├───┤ ░ ├─────────────┤ ░ ├───┤ ░ ├─────────────┤
q_1: ┤ H ├─░─┤ P(2.0*x[1]) ├─░─┤ H ├─░─┤ P(2.0*x[1]) ├─░─┤ H ├─░─┤ P(2.0*x[1]) ├
├───┤ ░ ├─────────────┤ ░ ├───┤ ░ ├─────────────┤ ░ ├───┤ ░ ├─────────────┤
q_2: ┤ H ├─░─┤ P(2.0*x[2]) ├─░─┤ H ├─░─┤ P(2.0*x[2]) ├─░─┤ H ├─░─┤ P(2.0*x[2]) ├
└───┘ ░ └─────────────┘ ░ └───┘ ░ └─────────────┘ ░ └───┘ ░ └─────────────┘>>> data_map = lambda x: x[0]*x[0] + 1 # note: input is an array
>>> prep = ZFeatureMap(3, reps=1, data_map_func=data_map)
>>> print(prep.decompose())
┌───┐┌──────────────────────┐
q_0: ┤ H ├┤ P(2.0*x[0]**2 + 2.0) ├
├───┤├──────────────────────┤
q_1: ┤ H ├┤ P(2.0*x[1]**2 + 2.0) ├
├───┤├──────────────────────┤
q_2: ┤ H ├┤ P(2.0*x[2]**2 + 2.0) ├
└───┘└──────────────────────┘>>> from qiskit.circuit.library import RealAmplitudes
>>> classifier = ZFeatureMap(3, reps=1).compose(RealAmplitudes(3, reps=1))
>>> print(classifier.decompose())
┌───┐┌─────────────┐┌──────────┐ ┌──────────┐
q_0: ┤ H ├┤ P(2.0*x[0]) ├┤ RY(θ[0]) ├─■──■─┤ RY(θ[3]) ├────────────
├───┤├─────────────┤├──────────┤ │ │ └──────────┘┌──────────┐
q_1: ┤ H ├┤ P(2.0*x[1]) ├┤ RY(θ[1]) ├─■──┼──────■──────┤ RY(θ[4]) ├
├───┤├─────────────┤├──────────┤ │ │ ├──────────┤
q_2: ┤ H ├┤ P(2.0*x[2]) ├┤ RY(θ[2]) ├────■──────■──────┤ RY(θ[5]) ├
└───┘└─────────────┘└──────────┘ └──────────┘Create a new first-order Pauli-Z expansion circuit.
Parameters
- feature_dimension (int) – The number of features
- reps (int) – The number of repeated circuits. Defaults to 2, has a minimum value of 1.
- data_map_func (Callable[[ndarray], float] | None) – A mapping function for data x which can be supplied to override the default mapping from
self_product(). - parameter_prefix (str) – The prefix used if default parameters are generated.
- insert_barriers (bool) – If True, barriers are inserted in between the evolution instructions and hadamard layers.
Attributes
alpha
The Pauli rotation factor (alpha).
Returns
The Pauli rotation factor.
ancillas
A list of AncillaQubits in the order that they were added. You should not mutate this.
calibrations
Return calibration dictionary.
The custom pulse definition of a given gate is of the form {'gate_name': {(qubits, params): schedule}}
clbits
A list of Clbits in the order that they were added. You should not mutate this.
data
The circuit data (instructions and context).
Returns
a list-like object containing the CircuitInstructions for each instruction.
Return type
QuantumCircuitData
entanglement
Get the entanglement strategy.
Returns
The entanglement strategy, see get_entangler_map() for more detail on how the format is interpreted.
entanglement_blocks
The blocks in the entanglement layers.
Returns
The blocks in the entanglement layers.
feature_dimension
Returns the feature dimension (which is equal to the number of qubits).
Returns
The feature dimension of this feature map.
flatten
Returns whether the circuit is wrapped in nested gates/instructions or flattened.
global_phase
The global phase of the current circuit scope in radians.
initial_state
Return the initial state that is added in front of the n-local circuit.
Returns
The initial state.
insert_barriers
If barriers are inserted in between the layers or not.
Returns
True, if barriers are inserted in between the layers, False if not.
instances
Default value: 164
layout
Return any associated layout information about the circuit
This attribute contains an optional TranspileLayout object. This is typically set on the output from transpile() or PassManager.run() to retain information about the permutations caused on the input circuit by transpilation.
There are two types of permutations caused by the transpile() function, an initial layout which permutes the qubits based on the selected physical qubits on the Target, and a final layout which is an output permutation caused by SwapGates inserted during routing.
metadata
Arbitrary user-defined metadata for the circuit.
Qiskit will not examine the content of this mapping, but it will pass it through the transpiler and reattach it to the output, so you can track your own metadata.
num_ancillas
Return the number of ancilla qubits.
num_captured_vars
The number of real-time classical variables in the circuit marked as captured from an enclosing scope.
This is the length of the iter_captured_vars() iterable. If this is non-zero, num_input_vars must be zero.
num_clbits
Return number of classical bits.
num_declared_vars
The number of real-time classical variables in the circuit that are declared by this circuit scope, excluding inputs or captures.
This is the length of the iter_declared_vars() iterable.
num_input_vars
The number of real-time classical variables in the circuit marked as circuit inputs.
This is the length of the iter_input_vars() iterable. If this is non-zero, num_captured_vars must be zero.
num_layers
Return the number of layers in the n-local circuit.
Returns
The number of layers in the circuit.
num_parameters
The number of parameter objects in the circuit.
num_parameters_settable
The number of distinct parameters.
num_qubits
Returns the number of qubits in this circuit.
Returns
The number of qubits.
num_vars
The number of real-time classical variables in the circuit.
This is the length of the iter_vars() iterable.
op_start_times
Return a list of operation start times.
This attribute is enabled once one of scheduling analysis passes runs on the quantum circuit.
Returns
List of integers representing instruction start times. The index corresponds to the index of instruction in QuantumCircuit.data.
Raises
AttributeError – When circuit is not scheduled.
ordered_parameters
The parameters used in the underlying circuit.
This includes float values and duplicates.
Examples
>>> # prepare circuit ...
>>> print(nlocal)
┌───────┐┌──────────┐┌──────────┐┌──────────┐
q_0: ┤ Ry(1) ├┤ Ry(θ[1]) ├┤ Ry(θ[1]) ├┤ Ry(θ[3]) ├
└───────┘└──────────┘└──────────┘└──────────┘
>>> nlocal.parameters
{Parameter(θ[1]), Parameter(θ[3])}
>>> nlocal.ordered_parameters
[1, Parameter(θ[1]), Parameter(θ[1]), Parameter(θ[3])]Returns
The parameters objects used in the circuit.
parameter_bounds
The parameter bounds for the unbound parameters in the circuit.
Returns
A list of pairs indicating the bounds, as (lower, upper). None indicates an unbounded parameter in the corresponding direction. If None is returned, problem is fully unbounded.
parameters
The parameters defined in the circuit.
This attribute returns the Parameter objects in the circuit sorted alphabetically. Note that parameters instantiated with a ParameterVector are still sorted numerically.
Examples
The snippet below shows that insertion order of parameters does not matter.
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> a, b, elephant = Parameter("a"), Parameter("b"), Parameter("elephant")
>>> circuit = QuantumCircuit(1)
>>> circuit.rx(b, 0)
>>> circuit.rz(elephant, 0)
>>> circuit.ry(a, 0)
>>> circuit.parameters # sorted alphabetically!
ParameterView([Parameter(a), Parameter(b), Parameter(elephant)])Bear in mind that alphabetical sorting might be unintuitive when it comes to numbers. The literal “10” comes before “2” in strict alphabetical sorting.
>>> from qiskit.circuit import QuantumCircuit, Parameter
>>> angles = [Parameter("angle_1"), Parameter("angle_2"), Parameter("angle_10")]
>>> circuit = QuantumCircuit(1)
>>> circuit.u(*angles, 0)
>>> circuit.draw()
┌─────────────────────────────┐
q: ┤ U(angle_1,angle_2,angle_10) ├
└─────────────────────────────┘
>>> circuit.parameters
ParameterView([Parameter(angle_1), Parameter(angle_10), Parameter(angle_2)])To respect numerical sorting, a ParameterVector can be used.
>>> from qiskit.circuit import QuantumCircuit, Parameter, ParameterVector
>>> x = ParameterVector("x", 12)
>>> circuit = QuantumCircuit(1)
>>> for x_i in x:
... circuit.rx(x_i, 0)
>>> circuit.parameters
ParameterView([
ParameterVectorElement(x[0]), ParameterVectorElement(x[1]),
ParameterVectorElement(x[2]), ParameterVectorElement(x[3]),
..., ParameterVectorElement(x[11])
])Returns
The sorted Parameter objects in the circuit.
paulis
The Pauli strings used in the entanglement of the qubits.
Returns
The Pauli strings as list.
preferred_init_points
The initial points for the parameters. Can be stored as initial guess in optimization.
Returns
The initial values for the parameters, or None, if none have been set.
prefix
Default value: 'circuit'
qregs
Type: list[QuantumRegister]
A list of the QuantumRegisters in this circuit. You should not mutate this.
qubits
A list of Qubits in the order that they were added. You should not mutate this.
reps
The number of times rotation and entanglement block are repeated.
Returns
The number of repetitions.
rotation_blocks
The blocks in the rotation layers.
Returns
The blocks in the rotation layers.
name
Type: str
A human-readable name for the circuit.
cregs
Type: list[ClassicalRegister]
A list of the ClassicalRegisters in this circuit. You should not mutate this.
duration
Type: int | float | None
The total duration of the circuit, set by a scheduling transpiler pass. Its unit is specified by unit.
unit
The unit that duration is specified in.