Skip to content

Latest commit

 

History

History
89 lines (55 loc) · 2.1 KB

workforcedqm.md

File metadata and controls

89 lines (55 loc) · 2.1 KB
Raw

D-Wave Systems Quantum Computing Training Project

Workforce Optimization Using Discrete Quadratic Models (DQM)

D-Wave Quantum Computer

Table of Contents

  1. Introduction
  2. Technologies Used
  3. Objective
  4. DQM Implementation
  5. Setup & Installation
  6. Results

Introduction

This repository is a showcase of a project focused on optimizing workforce distribution using Discrete Quadratic Models (DQM) via D-Wave Systems Quantum Computing.

Technologies Used

  • Python 3.x
  • D-Wave Ocean SDK

Objective

The primary aim of this project is to utilize D-Wave's Discrete Quadratic Models for workforce optimization, which involves the efficient allocation of employees to different shifts.

DQM Implementation

The crux of the project revolves around the implementation of D-Wave's Discrete Quadratic Model (DQM).

from dimod import DiscreteQuadraticModel

# Initialize the DQM object
dqm = DiscreteQuadraticModel()

Adding Variables for Employees

To build the DQM, we start by adding variables for each employee's name.

# Add variables to DQM for each employee
for name in employees:
    dqm.add_variable(num_shifts, label=name)

Explanation: Here dqm.add_variable(num_shifts, label=name) adds a variable representing each employee to the DQM. The variable has as many possible values as there are shifts (num_shifts), and it's labeled with the employee's name for identification.

Setup & Installation

To get started, clone this repository and install the D-Wave Ocean SDK.

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name
pip install dwave-ocean-sdk

Then, run the main Python script:

python main.py

Results

results, efficiency, and visualizations