# ExcitationPreserving#

class qiskit.circuit.library.ExcitationPreserving(num_qubits=None, mode='iswap', entanglement='full', reps=3, skip_unentangled_qubits=False, skip_final_rotation_layer=False, parameter_prefix='θ', insert_barriers=False, initial_state=None, name='ExcitationPreserving', flatten=None)[source]#

Bases: TwoLocal

The heuristic excitation-preserving wave function ansatz.

The ExcitationPreserving circuit preserves the ratio of $$|00\rangle$$, $$|01\rangle + |10\rangle$$ and $$|11\rangle$$ states. To this end, this circuit uses two-qubit interactions of the form

\begin{align}\begin{aligned}\newcommand{\th}{\theta/2}\\\begin{split}\begin{pmatrix} 1 & 0 & 0 & 0 \\ 0 & \cos\left(\th\right) & -i\sin\left(\th\right) & 0 \\ 0 & -i\sin\left(\th\right) & \cos\left(\th\right) & 0 \\ 0 & 0 & 0 & e^{-i\phi} \end{pmatrix}\end{split}\end{aligned}\end{align}

for the mode 'fsim' or with $$e^{-i\phi} = 1$$ for the mode 'iswap'.

Note that other wave functions, such as UCC-ansatzes, are also excitation preserving. However these can become complex quickly, while this heuristically motivated circuit follows a simpler pattern.

This trial wave function consists of layers of $$Z$$ rotations with 2-qubit entanglements. The entangling is creating using $$XX+YY$$ rotations and optionally a controlled-phase gate for the mode 'fsim'.

See RealAmplitudes for more detail on the possible arguments and options such as skipping unentanglement qubits, which apply here too.

The rotations of the ExcitationPreserving ansatz can be written as

Examples

>>> ansatz = ExcitationPreserving(3, reps=1, insert_barriers=True, entanglement='linear')
>>> print(ansatz)  # show the circuit
┌──────────┐ ░ ┌────────────┐┌────────────┐                             ░ ┌──────────┐
q_0: ┤ RZ(θ[0]) ├─░─┤0           ├┤0           ├─────────────────────────────░─┤ RZ(θ[5]) ├
├──────────┤ ░ │  RXX(θ[3]) ││  RYY(θ[3]) │┌────────────┐┌────────────┐ ░ ├──────────┤
q_1: ┤ RZ(θ[1]) ├─░─┤1           ├┤1           ├┤0           ├┤0           ├─░─┤ RZ(θ[6]) ├
├──────────┤ ░ └────────────┘└────────────┘│  RXX(θ[4]) ││  RYY(θ[4]) │ ░ ├──────────┤
q_2: ┤ RZ(θ[2]) ├─░─────────────────────────────┤1           ├┤1           ├─░─┤ RZ(θ[7]) ├
└──────────┘ ░                             └────────────┘└────────────┘ ░ └──────────┘

>>> ansatz = ExcitationPreserving(2, reps=1)
>>> qc = QuantumCircuit(2)  # create a circuit and append the RY variational form
>>> qc.cry(0.2, 0, 1)  # do some previous operation
>>> qc.compose(ansatz, inplace=True)  # add the swaprz
>>> qc.draw()
┌──────────┐┌────────────┐┌────────────┐┌──────────┐
q_0: ─────■─────┤ RZ(θ[0]) ├┤0           ├┤0           ├┤ RZ(θ[3]) ├
┌────┴────┐├──────────┤│  RXX(θ[2]) ││  RYY(θ[2]) │├──────────┤
q_1: ┤ RY(0.2) ├┤ RZ(θ[1]) ├┤1           ├┤1           ├┤ RZ(θ[4]) ├
└─────────┘└──────────┘└────────────┘└────────────┘└──────────┘

>>> ansatz = ExcitationPreserving(3, reps=1, mode='fsim', entanglement=[[0,2]],
... insert_barriers=True)
>>> print(ansatz)
┌──────────┐ ░ ┌────────────┐┌────────────┐        ░ ┌──────────┐
q_0: ┤ RZ(θ[0]) ├─░─┤0           ├┤0           ├─■──────░─┤ RZ(θ[5]) ├
├──────────┤ ░ │            ││            │ │      ░ ├──────────┤
q_1: ┤ RZ(θ[1]) ├─░─┤  RXX(θ[3]) ├┤  RYY(θ[3]) ├─┼──────░─┤ RZ(θ[6]) ├
├──────────┤ ░ │            ││            │ │θ[4]  ░ ├──────────┤
q_2: ┤ RZ(θ[2]) ├─░─┤1           ├┤1           ├─■──────░─┤ RZ(θ[7]) ├
└──────────┘ ░ └────────────┘└────────────┘        ░ └──────────┘

প্যারামিটার:
• num_qubits (int | None) -- The number of qubits of the ExcitationPreserving circuit.

• mode (str) -- Choose the entangler mode, can be 'iswap' or 'fsim'.

• reps (int) -- Specifies how often the structure of a rotation layer followed by an entanglement layer is repeated.

• entanglement (str | list[list[int]] | Callable[[int], list[int]]) -- Specifies the entanglement structure. Can be a string ('full', 'linear' 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. 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_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 (bool) -- If True, a rotation layer is added at the end of the ansatz. If False, no rotation layer is added. Defaults to True.

• 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.

রেইজেস:

ValueError -- If the selected mode is not supported.

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.

রিটার্নস:

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

entanglement_blocks#

The blocks in the entanglement layers.

রিটার্নস:

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.

initial_state#

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

রিটার্নস:

The initial state.

insert_barriers#

If barriers are inserted in between the layers or not.

রিটার্নস:

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.

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.

রিটার্নস:

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.

রিটার্নস:

The number of parameters originally available in the circuit.

নোট

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

num_qubits#

Returns the number of qubits in this circuit.

রিটার্নস:

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.

রিটার্নস:

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

রেইজেস:

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])]

রিটার্নস:

The parameters objects used in the circuit.

parameter_bounds#

Return the parameter bounds.

রিটার্নস:

The parameter bounds.

parameters#
preferred_init_points#

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

রিটার্নস:

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.

রিটার্নস:

The number of repetitions.

rotation_blocks#

The blocks in the rotation layers.

রিটার্নস:

The blocks in the rotation layers.