How To Implement Dwave Qbsolve In Python

Implementing D-Wave Qbsolv in Python

Quantum computing has been gaining a lot of attention in recent years as a possible solution for complex optimization problems. D-Wave is a company that provides access to quantum computers through the cloud. Qbsolv is a software tool that uses the D-Wave quantum computer to solve optimization problems. In this tutorial, we’ll show you how to solve optimization problems with Qbsolv and Python.

Problem Statement

Optimization problems involve finding the best solution among a set of possible solutions. There are a variety of optimization problems, and many of them are NP-hard, which means that they cannot be solved in a reasonable amount of time with classical computers. By using the D-Wave quantum computer, Qbsolv makes it easy to solve some optimization problems.

Installation

The first step is to install the necessary packages. We need to install the D-Wave Ocean SDK and the dwave-qbsolv package. We can install these packages using pip:

pip install dwave-ocean-sdk
pip install dwave-qbsolv

We also need to have access to a D-Wave quantum computer. We can create an account on the D-Wave website and obtain an API key. We will use this key to connect to the quantum computer through the cloud.

Solving an Optimization Problem

Now, we can solve an optimization problem using Qbsolv. The first step is to say that the problem is a quadratic unconstrained binary optimization (QUBO) problem. The QUBO problem involves minimizing a quadratic objective function subject to binary constraints. We show what the problem is by putting the coefficients of the quadratic objective function and the constraints into a dictionary.

Let’s consider a simple example of a QUBO problem:

Minimize the objective function f(x) = x₁ + x₂ – 2x₁x₂

subject to the constraints:

x₁ + x₂ ≤ 1

x₁, x₂ ∈ {0, 1}

We can represent this problem using a dictionary:

qubo = {(0, 0): 0, (0, 1): -2, (1, 1): 1, (0,): 0, (1,): 0}
bqm = dimod.BinaryQuadraticModel.from_qubo(qubo)

In this example, we define the QUBO problem as a dictionary called qubo. The keys of the dictionary are the variables, and the values are the coefficients of the objective function and the constraints. We create a binary quadratic model (BQM) using the BinaryQuadraticModel class of the dimod package. We pass the qubo dictionary to the from_qubo method to create the BQM object.

We can use the dwave-qbsolv package to solve the QUBO problem. We create a QbsolvSampler object and pass it the API key to connect to the D-Wave quantum computer through the cloud. We then use the sample method of the QbsolvSampler object to solve the problem. The method returns a SampleSet object that contains the solution.

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

client = Client.from_config(token='YOUR_API_KEY')
sampler = EmbeddingComposite(DWaveSampler())
qbsolv_sampler = QBSolv(sampler)

solution = qbsolv_sampler.sample(bqm)

print(solution)

In this example, we create a QbsolvSampler object called qbsolv_sampler. We pass a D-WaveSampler object wrapped in an EmbeddingComposite object to the QbsolvSampler object. The EmbeddingComposite object maps the QUBO problem’s logical qubits to the D-Wave quantum computer’s physical qubits. We use the sample method of the qbsolv_sampler object to solve the problem. The method returns a SampleSet object called solution.

Conclusion

In this tutorial, we showed you how to set up Qbsolv in Python so that you can use the D-Wave quantum computer to solve optimization problems. We installed the necessary packages, made a QUBO problem, and used the dwave-qbsolv package to solve the problem. We also connected to the D-Wave quantum computer through the cloud using an API key. Qbsolv provides an efficient way to solve certain optimization problems using quantum computing. However, it is important to note that not all optimization problems can be solved using Qbsolv, and the performance of the quantum computer may vary depending on the problem size and complexity.

Limitations and Caveats

Qbsolv is not good for all optimization problems, and how well it works depends on the size and complexity of the problem. Also, the D-Wave quantum computer is hard to get to, and it might not be available all the time. Finally, the results obtained from the quantum computer may be affected by noise and other factors, and it is important to validate the results using classical methods.