ZZFeatureMap
class qiskit.circuit.library.ZZFeatureMap(feature_dimension, reps=2, entanglement='full', data_map_func=None, parameter_prefix='x', insert_barriers=False, name='ZZFeatureMap')
Bases: PauliFeatureMap
Second-order Pauli-Z evolution circuit.
For 3 qubits and 1 repetition and linear entanglement the circuit is represented by:
┌───┐┌─────────────────┐
┤ H ├┤ U1(2.0*φ(x[0])) ├──■────────────────────────────■────────────────────────────────────
├───┤├─────────────────┤┌─┴─┐┌──────────────────────┐┌─┴─┐
┤ H ├┤ U1(2.0*φ(x[1])) ├┤ X ├┤ U1(2.0*φ(x[0],x[1])) ├┤ X ├──■────────────────────────────■──
├───┤├─────────────────┤└───┘└──────────────────────┘└───┘┌─┴─┐┌──────────────────────┐┌─┴─┐
┤ H ├┤ U1(2.0*φ(x[2])) ├──────────────────────────────────┤ X ├┤ U1(2.0*φ(x[1],x[2])) ├┤ X ├
└───┘└─────────────────┘ └───┘└──────────────────────┘└───┘where φ is a classical non-linear function, which defaults to φ(x) = x if and φ(x,y) = (pi - x)(pi - y).
Examples
>>> from qiskit.circuit.library import ZZFeatureMap
>>> prep = ZZFeatureMap(2, reps=1)
>>> print(prep)
┌───┐┌──────────────┐
q_0: ┤ H ├┤ U1(2.0*x[0]) ├──■───────────────────────────────────────■──
├───┤├──────────────┤┌─┴─┐┌─────────────────────────────────┐┌─┴─┐
q_1: ┤ H ├┤ U1(2.0*x[1]) ├┤ X ├┤ U1(2.0*(pi - x[0])*(pi - x[1])) ├┤ X ├
└───┘└──────────────┘└───┘└─────────────────────────────────┘└───┘>>> from qiskit.circuit.library import EfficientSU2
>>> classifier = ZZFeatureMap(3) + EfficientSU2(3)
>>> classifier.num_parameters
15
>>> classifier.parameters # 'x' for the data preparation, 'θ' for the SU2 parameters
ParameterView([
ParameterVectorElement(x[0]), ParameterVectorElement(x[1]),
ParameterVectorElement(x[2]), ParameterVectorElement(θ[0]),
ParameterVectorElement(θ[1]), ParameterVectorElement(θ[2]),
ParameterVectorElement(θ[3]), ParameterVectorElement(θ[4]),
ParameterVectorElement(θ[5]), ParameterVectorElement(θ[6]),
ParameterVectorElement(θ[7]), ParameterVectorElement(θ[8]),
ParameterVectorElement(θ[9]), ParameterVectorElement(θ[10]),
ParameterVectorElement(θ[11]), ParameterVectorElement(θ[12]),
ParameterVectorElement(θ[13]), ParameterVectorElement(θ[14]),
ParameterVectorElement(θ[15]), ParameterVectorElement(θ[16]),
ParameterVectorElement(θ[17]), ParameterVectorElement(θ[18]),
ParameterVectorElement(θ[19]), ParameterVectorElement(θ[20]),
ParameterVectorElement(θ[21]), ParameterVectorElement(θ[22]),
ParameterVectorElement(θ[23])
])
>>> classifier.count_ops()
OrderedDict([('ZZFeatureMap', 1), ('EfficientSU2', 1)])Create a new second-order Pauli-Z expansion.
Parameters
- feature_dimension (int) – Number of features.
- reps (int) – The number of repeated circuits, has a min. value of 1.
- entanglement (str |List[List[int]] | Callable[[int], List[int]]) – Specifies the entanglement structure. Refer to
NLocalfor detail. - data_map_func (Callable[[ndarray], float] | None) – A mapping function for data x.
- 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.
Raises
ValueError – If the feature dimension is smaller than 2.
Attributes
alpha
The Pauli rotation factor (alpha).
Returns
The Pauli rotation factor.
ancillas
Returns a list of ancilla bits in the order that the registers were added.
calibrations
Return calibration dictionary.
The custom pulse definition of a given gate is of the form {'gate_name': {(qubits, params): schedule}}
clbits
Returns a list of classical bits in the order that the registers were added.
data
entanglement
Get the entanglement strategy.
Returns
The entanglement strategy, see get_entangler_map() for more detail on how the format is interpreted.
entanglement_blocks
extension_lib
Default value: 'include "qelib1.inc";'
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
Return the global phase of the circuit in radians.
header
Default value: 'OPENQASM 2.0;'
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: 131
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
The user provided metadata associated with the circuit.
The metadata for the circuit is a user provided dict of metadata for the circuit. It will not be used to influence the execution or operation of the circuit, but it is expected to be passed between all transforms of the circuit (ie transpilation) and that providers will associate any circuit metadata with the results it returns from execution of that circuit.
num_ancillas
Return the number of ancilla qubits.
num_clbits
Return number of classical bits.
num_layers
Return the number of layers in the n-local circuit.
Returns
The number of layers in the circuit.
num_parameters
num_parameters_settable
The number of distinct parameters.
num_qubits
Returns the number of qubits in this circuit.
Returns
The number of qubits.
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
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 quantum registers associated with the circuit.
qubits
Returns a list of quantum bits in the order that the registers were added.
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.