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# (C) Copyright IBM 2017, 2020.
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"""The EfficientSU2 2-local circuit."""
from __future__ import annotations
import typing
from collections.abc import Callable
from numpy import pi
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library.standard_gates import RYGate, RZGate, CXGate
from .two_local import TwoLocal
if typing.TYPE_CHECKING:
import qiskit # pylint: disable=cyclic-import
[docs]class EfficientSU2(TwoLocal):
r"""The hardware efficient SU(2) 2-local circuit.
The ``EfficientSU2`` circuit consists of layers of single qubit operations spanned by SU(2)
and :math:`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 :math:`2 \times 2`
unitary matrices with determinant 1, such as the Pauli rotation gates.
On 3 qubits and using the Pauli :math:`Y` and :math:`Z` su2_gates as single qubit gates, the
hardware efficient SU(2) circuit is represented by:
.. parsed-literal::
ββββββββββββββββββββββββ β β β ββββββββββββββββββββββββββ
β€ RY(ΞΈ[0]) ββ€ RZ(ΞΈ[3]) ββββββββββββ βββββ ... ββββ€ RY(ΞΈ[12]) ββ€ RZ(ΞΈ[15]) β
ββββββββββββ€ββββββββββββ€ β βββ΄ββ β β βββββββββββββ€βββββββββββββ€
β€ RY(ΞΈ[1]) ββ€ RZ(ΞΈ[4]) βββββββ βββ€ X ββββ ... ββββ€ RY(ΞΈ[13]) ββ€ RZ(ΞΈ[16]) β
ββββββββββββ€ββββββββββββ€ β βββ΄βββββββ β β βββββββββββββ€βββββββββββββ€
β€ RY(ΞΈ[2]) ββ€ RZ(ΞΈ[5]) βββββ€ X βββββββββ ... ββββ€ RY(ΞΈ[14]) ββ€ RZ(ΞΈ[17]) β
ββββββββββββββββββββββββ β βββββ β β ββββββββββββββββββββββββββ
See :class:`~qiskit.circuit.library.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)
ββββββββββββββββββββββββ ββββββββββββββββββββββββ
q_0: β€ RY(ΞΈ[0]) ββ€ RZ(ΞΈ[3]) ββββ βββββ βββ€ RY(ΞΈ[6]) ββ€ RZ(ΞΈ[9]) ββββββββββββββ
ββββββββββββ€ββββββββββββ€βββ΄ββ β ββββββββββββββββββββββββ€βββββββββββββ
q_1: β€ RY(ΞΈ[1]) ββ€ RZ(ΞΈ[4]) ββ€ X ββββΌββββββββ βββββββ€ RY(ΞΈ[7]) ββ€ RZ(ΞΈ[10]) β
ββββββββββββ€ββββββββββββ€ββββββββ΄ββ βββ΄ββ ββββββββββββ€βββββββββββββ€
q_2: β€ RY(ΞΈ[2]) ββ€ RZ(ΞΈ[5]) βββββββ€ X βββββ€ X ββββββ€ RY(ΞΈ[8]) ββ€ RZ(ΞΈ[11]) β
ββββββββββββββββββββββββ βββββ βββββ βββββββββββββββββββββββββ
>>> 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()
ββββββββββββββββββββββ ββββββββββββ βββββ
q_0: β€ RX(ΞΈ[0]) ββ€ Y ββ€ X ββββ βββ€ RX(ΞΈ[4]) βββββ€ Y ββββββββββββββββββββββ
ββββββββββββ€βββββ€βββ¬βββββ΄ββββββββββββββββββ΄ββββ΄ββββ βββββ
q_1: β€ RX(ΞΈ[1]) ββ€ Y ββββΌβββ€ X βββββββ βββββββ€ RX(ΞΈ[5]) βββββ€ Y ββββββββββ
ββββββββββββ€βββββ€ β βββββ βββ΄ββ ββββββββββββββββ΄ββββ΄βββββββββ
q_2: β€ RX(ΞΈ[2]) ββ€ Y ββββΌβββββββββββ€ X βββββββββββ βββββββ€ RX(ΞΈ[6]) ββ€ Y β
ββββββββββββ€βββββ€ β βββββ βββ΄ββ ββββββββββββ€βββββ€
q_3: β€ RX(ΞΈ[3]) ββ€ Y ββββ βββββββββββββββββββββββ€ X ββββββ€ RX(ΞΈ[7]) ββ€ Y β
βββββββββββββββββ βββββ βββββββββββββββββ
"""
def __init__(
self,
num_qubits: int | None = None,
su2_gates: str
| type
| qiskit.circuit.Instruction
| QuantumCircuit
| list[str | type | qiskit.circuit.Instruction | QuantumCircuit]
| None = None,
entanglement: str | list[list[int]] | Callable[[int], list[int]] = "reverse_linear",
reps: int = 3,
skip_unentangled_qubits: bool = False,
skip_final_rotation_layer: bool = False,
parameter_prefix: str = "ΞΈ",
insert_barriers: bool = False,
initial_state: QuantumCircuit | None = None,
name: str = "EfficientSU2",
flatten: bool | None = None,
) -> None:
"""
Args:
num_qubits: The number of qubits of the EfficientSU2 circuit.
reps: Specifies how often the structure of a rotation layer followed by an entanglement
layer is repeated.
su2_gates: 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: 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 :class:`~qiskit.circuit.library.TwoLocal` for more
detail.
initial_state: A `QuantumCircuit` object to prepend to the circuit.
skip_unentangled_qubits: 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: If False, a rotation layer is added at the end of the
ansatz. If True, no rotation layer is added.
parameter_prefix: The parameterized gates require a parameter to be defined, for which
we use :class:`~qiskit.circuit.ParameterVector`.
insert_barriers: If True, barriers are inserted in between each layer. If False,
no barriers are inserted.
flatten: 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.
"""
if su2_gates is None:
su2_gates = [RYGate, RZGate]
super().__init__(
num_qubits=num_qubits,
rotation_blocks=su2_gates,
entanglement_blocks=CXGate,
entanglement=entanglement,
reps=reps,
skip_unentangled_qubits=skip_unentangled_qubits,
skip_final_rotation_layer=skip_final_rotation_layer,
parameter_prefix=parameter_prefix,
insert_barriers=insert_barriers,
initial_state=initial_state,
name=name,
flatten=flatten,
)
@property
def parameter_bounds(self) -> list[tuple[float, float]]:
"""Return the parameter bounds.
Returns:
The parameter bounds.
"""
return self.num_parameters * [(-pi, pi)]