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 \(CX\) 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 \(2 \times 2\) unitary matrices with determinant 1, such as the Pauli rotation gates.

On 3 qubits and using the Pauli \(Y\) and \(Z\) 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)
     ┌──────────┐┌──────────┐          ┌──────────┐┌──────────┐
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 ├
     └──────────┘└───┘                         └───┘    └──────────┘└───┘

Parâmetros:

• 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”, “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. Default 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

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.

Retorno:

The entanglement strategy, see get_entangler_map() for more detail on how the format is interpreted.

entanglement_blocks

The blocks in the entanglement layers.

Retorno:

The blocks in the entanglement layers.

extension_lib = 'include "qelib1.inc";'

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 = 'OPENQASM 2.0;'

initial_state

Return the initial state that is added in front of the n-local circuit.

Retorno:

The initial state.

insert_barriers

If barriers are inserted in between the layers or not.

Retorno:

True, if barriers are inserted in between the layers, False if not.

instances = 127

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.

Retorno:

The number of layers in the circuit.

num_parameters

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.

Retorno:

The number of parameters originally available in the circuit.

Nota

This quantity does not require the circuit to be built yet.

num_qubits

Returns the number of qubits in this circuit.

Retorno:

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.

Retorno:

List of integers representing instruction start times. The index corresponds to the index of instruction in QuantumCircuit.data.

Levanta:

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])]

Retorno:

The parameters objects used in the circuit.

parameter_bounds

Return the parameter bounds.

Retorno:

The parameter bounds.

parameters

preferred_init_points

The initial points for the parameters. Can be stored as initial guess in optimization.

Retorno:

The initial values for the parameters, or None, if none have been set.

prefix = 'circuit'

qregs: 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.

Retorno:

The number of repetitions.

rotation_blocks

The blocks in the rotation layers.

Retorno:

The blocks in the rotation layers.