EfficientSU2
class qiskit.circuit.library.EfficientSU2(num_qubits=None, su2_gates=None, entanglement='reverse_linear', reps=3, skip_unentangled_qubits=False, skip_final_rotation_layer=False, parameter_prefix='θ', insert_barriers=False, initial_state=None, name='EfficientSU2', flatten=None)
Bases: TwoLocal
The hardware efficient SU(2) 2-local circuit.
The EfficientSU2 circuit consists of layers of single qubit operations spanned by SU(2) and entanglements. This is a heuristic pattern that can be used to prepare trial wave functions for variational quantum algorithms or classification circuit for machine learning.
SU(2) stands for special unitary group of degree 2, its elements are unitary matrices with determinant 1, such as the Pauli rotation gates.
On 3 qubits and using the Pauli and su2_gates as single qubit gates, the hardware efficient SU(2) circuit is represented by:
┌──────────┐┌──────────┐ ░ ░ ░ ┌───────────┐┌───────────┐
┤ RY(θ[0]) ├┤ RZ(θ[3]) ├─░────────■───░─ ... ─░─┤ RY(θ[12]) ├┤ RZ(θ[15]) ├
├──────────┤├──────────┤ ░ ┌─┴─┐ ░ ░ ├───────────┤├───────────┤
┤ RY(θ[1]) ├┤ RZ(θ[4]) ├─░───■──┤ X ├─░─ ... ─░─┤ RY(θ[13]) ├┤ RZ(θ[16]) ├
├──────────┤├──────────┤ ░ ┌─┴─┐└───┘ ░ ░ ├───────────┤├───────────┤
┤ RY(θ[2]) ├┤ RZ(θ[5]) ├─░─┤ X ├──────░─ ... ─░─┤ RY(θ[14]) ├┤ RZ(θ[17]) ├
└──────────┘└──────────┘ ░ └───┘ ░ ░ └───────────┘└───────────┘See RealAmplitudes for more detail on the possible arguments and options such as skipping unentanglement qubits, which apply here too.
Examples
>>> circuit = EfficientSU2(3, reps=1)
>>> print(circuit.decompose())
┌──────────┐┌──────────┐ ┌──────────┐┌──────────┐
q_0: ┤ RY(θ[0]) ├┤ RZ(θ[3]) ├──■────■──┤ RY(θ[6]) ├┤ RZ(θ[9]) ├─────────────
├──────────┤├──────────┤┌─┴─┐ │ └──────────┘├──────────┤┌───────────┐
q_1: ┤ RY(θ[1]) ├┤ RZ(θ[4]) ├┤ X ├──┼───────■──────┤ RY(θ[7]) ├┤ RZ(θ[10]) ├
├──────────┤├──────────┤└───┘┌─┴─┐ ┌─┴─┐ ├──────────┤├───────────┤
q_2: ┤ RY(θ[2]) ├┤ RZ(θ[5]) ├─────┤ X ├───┤ X ├────┤ RY(θ[8]) ├┤ RZ(θ[11]) ├
└──────────┘└──────────┘ └───┘ └───┘ └──────────┘└───────────┘>>> ansatz = EfficientSU2(4, su2_gates=['rx', 'y'], entanglement='circular', reps=1,
... flatten=True)
>>> qc = QuantumCircuit(4) # create a circuit and append the RY variational form
>>> qc.compose(ansatz, inplace=True)
>>> qc.draw()
┌──────────┐┌───┐┌───┐ ┌──────────┐ ┌───┐
q_0: ┤ RX(θ[0]) ├┤ Y ├┤ X ├──■──┤ RX(θ[4]) ├───┤ Y ├─────────────────────
├──────────┤├───┤└─┬─┘┌─┴─┐└──────────┘┌──┴───┴───┐ ┌───┐
q_1: ┤ RX(θ[1]) ├┤ Y ├──┼──┤ X ├─────■──────┤ RX(θ[5]) ├───┤ Y ├─────────
├──────────┤├───┤ │ └───┘ ┌─┴─┐ └──────────┘┌──┴───┴───┐┌───┐
q_2: ┤ RX(θ[2]) ├┤ Y ├──┼──────────┤ X ├─────────■──────┤ RX(θ[6]) ├┤ Y ├
├──────────┤├───┤ │ └───┘ ┌─┴─┐ ├──────────┤├───┤
q_3: ┤ RX(θ[3]) ├┤ Y ├──■──────────────────────┤ X ├────┤ RX(θ[7]) ├┤ Y ├
└──────────┘└───┘ └───┘ └──────────┘└───┘The efficient_su2() function constructs a functionally equivalent circuit, but faster.
The class qiskit.circuit.library.n_local.efficient_su2.EfficientSU2 is pending deprecation as of qiskit 1.3. It will be marked deprecated in a future release, and then removed no earlier than 3 months after the release date. Use the function qiskit.circuit.library.efficient_su2 instead.
Parameters
- num_qubits (int | None) – The number of qubits of the EfficientSU2 circuit.
- reps (int) – Specifies how often the structure of a rotation layer followed by an entanglement layer is repeated.
- su2_gates (str |type |qiskit.circuit.Instruction |QuantumCircuit |list[str |type |qiskit.circuit.Instruction |QuantumCircuit] | None) – The SU(2) single qubit gates to apply in single qubit gate layers. If only one gate is provided, the same gate is applied to each qubit. If a list of gates is provided, all gates are applied to each qubit in the provided order.
- entanglement (str |list[list[int]] | Callable[[int], list[int]]) – Specifies the entanglement structure. Can be a string (‘full’, ‘linear’, ‘reverse_linear’, ‘pairwise’, ‘circular’, or ‘sca’), a list of integer-pairs specifying the indices of qubits entangled with one another, or a callable returning such a list provided with the index of the entanglement layer. Defaults to ‘reverse_linear’ entanglement. Note that ‘reverse_linear’ entanglement provides the same unitary as ‘full’ with fewer entangling gates. See the Examples section of
TwoLocalfor more detail. - initial_state (QuantumCircuit | None) – A QuantumCircuit object to prepend to the circuit.
- skip_unentangled_qubits (bool) – If True, the single qubit gates are only applied to qubits that are entangled with another qubit. If False, the single qubit gates are applied to each qubit in the Ansatz. Defaults to False.
- skip_final_rotation_layer (bool) – If False, a rotation layer is added at the end of the ansatz. If True, no rotation layer is added.
- parameter_prefix (str) – The parameterized gates require a parameter to be defined, for which we use
ParameterVector. - insert_barriers (bool) – If True, barriers are inserted in between each layer. If False, no barriers are inserted.
- flatten (bool | None) – Set this to
Trueto output a flat circuit instead of nesting it inside multiple layers of gate objects. By default currently the contents of the output circuit will be wrapped in nested objects for cleaner visualization. However, if you’re using this circuit for anything besides visualization its strongly recommended to set this flag toTrueto avoid a large performance overhead for parameter binding. - name (str) –
Attributes
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}}
The property qiskit.circuit.quantumcircuit.QuantumCircuit.calibrations is deprecated as of Qiskit 1.3. It will be removed in Qiskit 2.0. The entire Qiskit Pulse package is being deprecated and will be moved to the Qiskit Dynamics repository: https://github.com/qiskit-community/qiskit-dynamics. Note that once removed, qiskit.circuit.quantumcircuit.QuantumCircuit.calibrations will have no alternative in Qiskit.
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
duration
The total duration of the circuit, set by a scheduling transpiler pass. Its unit is specified by unit.
The property qiskit.circuit.quantumcircuit.QuantumCircuit.duration is deprecated as of qiskit 1.3.0. It will be removed in Qiskit 2.0.0.
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.
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: 157
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 total parameters that can be set to distinct values.
This does not change when the parameters are bound or exchanged for same parameters, and therefore is different from num_parameters which counts the number of unique Parameter objects currently in the circuit.
Returns
The number of parameters originally available in the circuit.
This quantity does not require the circuit to be built yet.
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
Return the parameter bounds.
Returns
The parameter bounds.
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.
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.
unit
The unit that duration is specified in.
The property qiskit.circuit.quantumcircuit.QuantumCircuit.unit is deprecated as of qiskit 1.3.0. It will be removed in Qiskit 2.0.0.
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.