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)

GitHub

Bases: TwoLocal

The hardware efficient SU(2) 2-local circuit.

CX

2 \times 2

Y

┌──────────┐┌──────────┐ ░            ░       ░ ┌───────────┐┌───────────┐
┤ 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)
     ┌──────────┐┌──────────┐          ┌──────────┐┌──────────┐
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)
>>> 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 ├
     └──────────┘└───┘                         └───┘    └──────────┘└───┘

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 TwoLocal for 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 True to 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 to True to avoid a large performance overhead for parameter binding.

Attributes

ancillas

A list of AncillaQubit s 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 Clbit s 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 CircuitInstruction s 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.

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: 160

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 SwapGate s 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.

Note

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 QuantumRegister s in this circuit. You should not mutate this.

qubits

A list of Qubit s 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 ClassicalRegister s 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.

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