---
title: qaoa_ansatz (latest version)
description: API reference for qiskit.circuit.library.qaoa_ansatz in the latest version of qiskit
source: https://eu-de.quantum.cloud.ibm.com/docs/en/api/qiskit/qiskit.circuit.library.qaoa_ansatz
---

# qiskit.circuit.library.qaoa\_ansatz

`qiskit.circuit.library.qaoa_ansatz(cost_operator, reps=1, initial_state=None, mixer_operator=None, insert_barriers=False, name='QAOA', flatten=True)`

[GitHub](https://github.com/Qiskit/qiskit/tree/stable/2.5/qiskit/circuit/library/n_local/qaoa_ansatz.py#L33-L105)

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

Examples:

To define the QAOA ansatz we require a cost Hamiltonian, encoding the classical optimization problem:

```python
from qiskit.quantum_info import SparsePauliOp
from qiskit.circuit.library import qaoa_ansatz

cost_operator = SparsePauliOp(["ZZII", "IIZZ", "ZIIZ"])
ansatz = qaoa_ansatz(cost_operator, reps=3, insert_barriers=True)
ansatz.draw("mpl")
```

![Circuit diagram output by the previous code.](https://eu-de.quantum.cloud.ibm.com/docs/images/api/qiskit/qiskit-circuit-library-qaoa_ansatz-1.avif)

**Parameters**

- **cost\_operator** (*BaseOperator*) – The operator representing the cost of the optimization problem, denoted as $U(C, \gamma)$ in \[1].
- **reps** ([*int*](https://docs.python.org/3/library/functions.html#int)) – The integer determining the depth of the circuit, called $p$ in \[1].
- **initial\_state** ([*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit "qiskit.circuit.quantumcircuit.QuantumCircuit") *| None*) – An optional initial state to use, which defaults to a layer of Hadamard gates preparing the $|+\rangle^{\otimes n}$ state. If a custom mixer is chosen, this circuit should be set to prepare its ground state, to appropriately fulfill the annealing conditions.
- **mixer\_operator** (*BaseOperator | None*) – An optional custom mixer, which defaults to global Pauli-$X$ rotations. This is denoted as $U(B, \beta)$ in \[1]. If this is set, the `initial_state` might also require modification.
- **insert\_barriers** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – Whether to insert barriers in-between the cost and mixer operators.
- **name** ([*str*](https://docs.python.org/3/library/stdtypes.html#str)) – The name of the circuit.
- **flatten** ([*bool*](https://docs.python.org/3/library/functions.html#bool)) – If `True`, a flat circuit is returned instead of nesting it inside multiple layers of gate objects. Setting this to `False` is significantly less performant, especially for parameter binding, but can be desirable for a cleaner visualization.

**Return type**

[*QuantumCircuit*](/docs/api/qiskit/qiskit.circuit.QuantumCircuit "qiskit.circuit.quantumcircuit.QuantumCircuit")

References:

\[1] Farhi et al., A Quantum Approximate Optimization Algorithm. [arXiv:1411.4028](https://arxiv.org/pdf/1411.4028)