AWS

QAOA - Quantum Approximate Optimization Algorithm

The Quantum Approximate Optimization Algorithm (QAOA) 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.

Available
When running QAOA on IBM's free plan, runtime errors may occur. This is due to IBM prioritizing jobs from users on paid plans, which can result in session timeouts for users waiting in the public queue.

Provider: aws

Solution Example:

Parameters:

aws_provider : str

QPU provider name from Amazon Braket. Available providers and devices can be found here.

Default: None

aws_device : str

QPU device name from Amazon Braket. Available providers and devices can be found here.

Default: None

seed : int

Seed for the random number generator.

Default: 385920

reps : int

The number of repetitions in the QAOA circuit.

Default: 1

initial_values : Listfloat

Initial values for the QAOA parameters.

Default: None

shots : int

The number of shots to run on the quantum device.

Default: 1024

optimizer_params : OptimizerParams

Parameters for the optimizer. Default: None. All possible optimizer parameters can be found in the scipy.optimize.minimize documentation.

Default:

method : 'Nelder-Mead', 'Powell', 'CG', 'BFGS', 'L-BFGS-B', 'TNC', 'COBYLA', 'COBYQA', 'SLSQP', 'trust-constr'

Type of solver. Currently available methods: Nelder-Mead, Powell, CG, BFGS, L-BFGS-B, TNC, COBYLA, COBYQA, SLSQP, trust-constr

Default: "COBYLA"

bounds : listtuplefloat, float

Bounds on variables for Nelder-Mead, L-BFGS-B, TNC, SLSQP, Powell, trust-constr, and COBYLA methods. Sequence of (min, max) pairs for each element in x. None is used to specify no bound.

Default: None

tol : float

Tolerance for termination. When tol is specified, the selected minimization algorithm sets some relevant solver-specific tolerance(s) equal to tol. For detailed control, use solver-specific options.

Default: None

options : Any

A dictionary of solver options.

Default:

maxiter :

Default: None