QAOAAnsatz
class qiskit.circuit.library.QAOAAnsatz(cost_operator=None, reps=1, initial_state=None, mixer_operator=None, name='QAOA', flatten=None)
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(opens in a new tab)
Parameters
- cost_operator (BaseOperator or OperatorBase, optional) – The operator representing the cost of the optimization problem, denoted as in the original paper. Must be set either in the constructor or via property setter.
- reps (int(opens in a new tab)) – 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 in the original paper. Can be an operator or an optionally parameterized quantum circuit.
- name (str(opens in a new tab)) – A name of the circuit, default ‘qaoa’
- flatten (bool(opens in a new tab) | None) – Set this to
Trueto 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 toTrueto avoid a large performance overhead for parameter binding.
Attributes
ancillas
A list of AncillaQubits 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 Clbits in the order that they were added. You should not mutate this.
cost_operator
Returns an operator representing the cost of the optimization problem.
Returns
cost operator.
Return type
BaseOperator or OperatorBase
data
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.
evolution
The evolution converter used to compute the evolution.
Returns
The evolution converter used to compute the evolution.
Return type
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
Returns an optional initial state as a circuit
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: 316
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
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.
mixer_operator
Returns an optional mixer operator expressed as an operator or a quantum circuit.
Returns
mixer operator or circuit.
Return type
BaseOperator or OperatorBase or QuantumCircuit, optional
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
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.
This quantity does not require the circuit to be built yet.
num_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(opens in a new tab) – When circuit is not scheduled.
operators
The operators that are evolved in this circuit.
Returns
The operators to be evolved
(and circuits) in this ansatz.
Return type
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])]Returns
The parameters objects used in the circuit.
parameter_bounds
The parameter bounds for the unbound parameters in the circuit.
Returns
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
Default value: 'circuit'
qregs
Type: list[QuantumRegister]
A list of the QuantumRegisters in this circuit. You should not mutate this.
qubits
A list of Qubits in the order that they were added. You should not mutate this.
reps
Returns the reps parameter, which determines the depth of the circuit.
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 ClassicalRegisters 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.