AWS

QAOA - Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm ([QAOA](https://arxiv.org/abs/1411.4028)) solves combinatorial optimization problems by approximating the solution. The Quantum Approximate Optimization Algorithm (QAOA) belongs to the class of hybrid quantum algorithms (leveraging both classical as well as quantum compute), that are widely believed to be the working horse for the current NISQ (noisy intermediate-scale quantum) era. In this NISQ era QAOA is also an emerging approach for benchmarking quantum devices and is a prime candidate for demonstrating a practical quantum speed-up on near-term NISQ device.

Provider:

aws

Solution Example:

## Example of creating solution for quantum_approximate_optimization_algorithm (QAOA) from aws solution = ls.solution.create( optimization_id=optimization.id, solver_name="QAOA", provider="aws", parameters={}, qpu_tokens={} )

Parameters:

aws_provider: str

QPU provider name from Amazon Braket. Available providers and devices can be found [here](https://us-east-1.console.aws.amazon.com/braket/home?region=us-east-1#/devices).

Default: None

aws_device: str

QPU device name from Amazon Braket. Available providers and devices can be found [here](https://us-east-1.console.aws.amazon.com/braket/home?region=us-east-1#/devices).

Default: None

seed: int

Seed for the random number generator. Default: 385920.

Default: 385920

reps: int

The number of repetitions in the QAOA circuit. Default: 1.

Default: 1

initial_values: typing.List[float]

Initial values for the QAOA parameters. Default: None.

Default: None

shots: int

The number of shots to run on the quantum device. Default: 1024.

Default: 1024

optimizer_params: dict

Parameters for the optimizer. Default: None. All possible optimizer parameters can be found in the [scipy.optimize.minimize documentation](https://docs.scipy.org/doc/scipy-1.13.1/reference/generated/scipy.optimize.minimize.html).

Default: None