# QAOAAnsatz

`qiskit.circuit.library.QAOAAnsatz(cost_operator=None, reps=1, initial_state=None, mixer_operator=None, name='QAOA', flatten=None)`

GitHub(opens in a new tab)

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 $U(C, \gamma)$ 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 $U(B, \beta)$ 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`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.

**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

Return 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

`= 266`

### 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 `SwapGate`

s 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.

**Returns**

mixer operator or circuit.

**Return type**

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.

**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

### 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

`= '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.

**Returns**

The blocks in the rotation layers.