Technology

# How to Implement DWave Qbsolve in Python?

To implement DWave Qbsolve in Python, you can follow these steps:

## Step 1:

Install the required libraries You need to install the following libraries: dwave-cloud-client, dwave-system, and dwave-qbsolv. You can use the following command to install them:

pip install dwave-cloud-client dwave-system dwave-qbsolv

## Step 2:

Set up the DWave Cloud Client Before using DWave Qbsolve, you need to set up the DWave Cloud Client with your API token. You can do this by running the following code:

from dwave.cloud import Client

client = Client.from_config(token=’YOUR_API_TOKEN’)

Replace `YOUR_API_TOKEN` with your actual API token.

## Step 3:

Define the QUBO problem QUBO (quadratic unconstrained binary optimization) is a problem in which the objective function is a quadratic polynomial of binary variables. You need to define the QUBO problem that you want to solve using DWave Qbsolve. Here is an example:

from dwave.system.samplers import DWaveSampler
from dwave.system.composites import EmbeddingComposite
from dwave_qbsolv import QBSolv

Q = {(0, 0): -1, (0, 1): 2, (1, 1): -1}
response = QBSolv().sample_qubo(Q)
print(response)

In this example, we define a QUBO problem with three binary variables (0, 1, and 2). The objective function is:

-1×0 + 2x0x1 – 1x1x2

We use the QBSolv function to solve the problem. The function returns a response object that contains the solutions to the problem.

## Step 4:

Submit the problem to DWave Qbsolve Finally, you need to submit the problem to DWave Qbsolve using the following code:

sampler = EmbeddingComposite(DWaveSampler())
response = QBSolv().sample_qubo(Q, solver=sampler)
print(response)

In this code, we use the `EmbeddingComposite` function to embed the QUBO problem onto the D-Wave quantum annealer. We also pass the `DWaveSampler` as the solver to the `sample_qubo` function.

That’s it! You have now implemented DWave Qbsolve in Python.