QAOAAnsatz#

class qiskit.circuit.library.QAOAAnsatz(cost_operator=None, reps=1, initial_state=None, mixer_operator=None, name='QAOA', flatten=None)[código fonte]#

Bases: EvolvedOperatorAnsatz

A generalized QAOA quantum circuit with a support of custom initial states and mixers.

References

[1]: Farhi et al., A Quantum Approximate Optimization Algorithm.: arXiv:1411.4028

Parâmetros:

cost_operator (BaseOperator or OperatorBase, optional) – The operator representing the cost of the optimization problem, denoted as \(U(C, \gamma)\) in the original paper. Must be set either in the constructor or via property setter.
reps (int) – The integer parameter p, which determines the depth of the circuit, as specified in the original paper, default is 1.
initial_state (QuantumCircuit, optional) – An optional initial state to use. If None is passed then a set of Hadamard gates is applied as an initial state to all qubits.
mixer_operator (BaseOperator or OperatorBase or QuantumCircuit, optional) – An optional custom mixer to use instead of the global X-rotations, denoted as \(U(B, \beta)\) in the original paper. Can be an operator or an optionally parameterized quantum circuit.
name (str) – A name of the circuit, default “qaoa”
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.

cost_operator#

Returns an operator representing the cost of the optimization problem.

Retorno:: cost operator.
Tipo de retorno:: BaseOperator or OperatorBase

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.

evolution#

The evolution converter used to compute the evolution.

Retorno:: The evolution converter used to compute the evolution.
Tipo de retorno:: EvolutionBase or EvolutionSynthesis

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#: Returns an optional initial state as a circuit

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 = 410#

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.

mixer_operator#

Returns an optional mixer operator expressed as an operator or a quantum circuit.

Retorno:: mixer operator or circuit.
Tipo de retorno:: BaseOperator or OperatorBase or QuantumCircuit, optional

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#

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.

operators#

The operators that are evolved in this circuit.

Retorno:

The operators to be evolved: (and circuits) in this ansatz.

Tipo de retorno:

List[Union[BaseOperator, OperatorBase, QuantumCircuit]]

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#

The parameter bounds for the unbound parameters in the circuit.

Retorno:: A list of pairs indicating the bounds, as (lower, upper). None indicates an unbounded parameter in the corresponding direction. If None is returned, problem is fully unbounded.

parameters#

preferred_init_points#: Getter of preferred initial points based on the given initial state.

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#: Returns the reps parameter, which determines the depth of the circuit.

rotation_blocks#

The blocks in the rotation layers.

Retorno:: The blocks in the rotation layers.