# How to implement D-Wave Qbsolve in Python? (The Easy Way)

In this post, we will explore **how to implement D-Wave Qbsolve in Python**. Basically, Quantum computing is an emerging technology that has the potential to revolutionize various fields, including finance, cryptography, drug discovery, and more. D-Wave Systems Inc. is one of the leading companies that provide commercial quantum computing systems. One of its most popular tools is Qbsolv, which is an open-source software package that is used to solve quadratic unconstrained binary optimization (QUBO) problems.

## What is Qbsolve?

Qbsolv is a tool that is used to solve QUBO problems. A QUBO problem is a type of optimization problem in which we need to minimize a quadratic function of binary variables. The binary variables can only take two values, i.e., 0 and 1. In a QUBO problem, we are given a quadratic function of binary variables, and we need to find the binary variable values that minimize the function. Qbsolv is an open-source tool that can be used to solve QUBO problems of any size.

## Installing D-Wave Qbsolve

We must first install D-Wave Qbsolve on our system before we can begin utilizing it. The easiest way to install Qbsolve is through pip. Open your command prompt and run the following command:

pip install dwave-qbsolv

Once the installation is complete, we can start using D-Wave Qbsolve in Python.

## Using D-Wave Qbsolve

To use D-Wave Qbsolve, we first need to define our QUBO problem. We can do this by defining a matrix of coefficients that represents the quadratic function of binary variables. The matrix should be a symmetric matrix, and the diagonal elements should be the coefficients of the linear terms. Consider the QUBO problem described below:

minimize x1^2 - 2x1x2 + 3x2^2 - 4x1 - 5*x2

We can represent this problem as a matrix of coefficients:

| 1 -2 | |-2 3 |

The diagonal elements of the matrix represent the coefficients of the linear terms, and the off-diagonal elements represent the coefficients of the quadratic terms.

Once we have defined our QUBO problem, we can use Qbsolv to solve it. We can use the qbsolv() function provided by the dwave-qbsolv package to solve the problem. The qbsolv() function takes two arguments: the matrix of coefficients and a parameter called num_reads, which determines the number of times Qbsolv will run the problem.

Here is an example of Python code that uses Qbsolv to solve the above QUBO problem:

import dwave_qbsolv as qbsolv # Define the matrix of coefficients Q = [[1, -2], [-2, 3]] # Set the num_reads parameter num_reads = 1000 # Solve the QUBO problem using Qbsolv response = qbsolv.qbsolv(Q, num_reads=num_reads) # Print the results print(response)

The output of the above code will be a dictionary containing the solutions and energies of the problem. The solutions are represented as binary strings, and the energies are the corresponding values of the quadratic function.

## Parallel Qbsolv

Qbsolv can also be run in parallel mode, which allows us to solve multiple QUBO problems simultaneously. This can significantly speed up the solving process, especially for large QUBO problems. To use Qbsolv in parallel mode, we need to define a list of matrices, where each matrix represents a QUBO problem.

Here is an example of Python code that uses Qbsolv in parallel mode:

import dwave_qbsolv as qbsolv # Define the matrix of coefficients Q = [[1, -2], [-2, 3]] # Set the num_reads parameter num_reads = 1000 # Solve the QUBO problem using Qbsolv response = qbsolv.qbsolv(Q, num_reads=num_reads) # Print the results print(response)

The output of the above code will be a dictionary containing the solutions and energies of the problem. The solutions are represented as binary strings, and the energies are the corresponding values of the quadratic function.

## Conclusion

D-Wave Qbsolve is a powerful tool for solving QUBO problems of any size. It can be easily installed using pip and used in Python to solve quadratic optimization problems. By defining a matrix of coefficients that represents the quadratic function of binary variables, we can use Qbsolv to find the binary variable values that minimize the function. Qbsolv can also be run in parallel mode to solve multiple QUBO problems simultaneously, which can significantly speed up the solving process.