https://github.com/meet-c-viradiya/quantum-workflow
Quantum-enhanced workflow scheduling optimizes multi-processor resource allocation using DAGs and a hybrid quantum-classical approach, visualized via advanced tools. QUBO problem formulation ensures efficiency.
https://github.com/meet-c-viradiya/quantum-workflow
cpp drug-design hpc machinelearning-python nisq pipelines qiskit qiskit-metal quantum-algorithms quantum-chemistry quantum-machine-learning transmon-qubit unitaryhack workflow-automation
Last synced: 5 months ago
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Quantum-enhanced workflow scheduling optimizes multi-processor resource allocation using DAGs and a hybrid quantum-classical approach, visualized via advanced tools. QUBO problem formulation ensures efficiency.
- Host: GitHub
- URL: https://github.com/meet-c-viradiya/quantum-workflow
- Owner: meet-c-viradiya
- Created: 2025-04-27T15:58:58.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-30T13:56:32.000Z (5 months ago)
- Last Synced: 2025-04-30T14:28:14.671Z (5 months ago)
- Topics: cpp, drug-design, hpc, machinelearning-python, nisq, pipelines, qiskit, qiskit-metal, quantum-algorithms, quantum-chemistry, quantum-machine-learning, transmon-qubit, unitaryhack, workflow-automation
- Language: Python
- Size: 741 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Quantum Workflow 🚀
 [](https://github.com/meet-c-viradiya/quantum-workflow/releases)
Welcome to **Quantum Workflow**, a project designed to enhance workflow scheduling using quantum computing techniques. This repository offers tools for optimizing multi-processor resource allocation through directed acyclic graphs (DAGs) and a hybrid quantum-classical approach. The efficient QUBO problem formulation lies at the core of our solution, ensuring that your scheduling tasks are handled effectively.
## Table of Contents
1. [Introduction](#introduction)
2. [Features](#features)
3. [Getting Started](#getting-started)
4. [Installation](#installation)
5. [Usage](#usage)
6. [Example](#example)
7. [Contributing](#contributing)
8. [License](#license)
9. [Contact](#contact)
10. [Releases](#releases)
## Introduction
In today's fast-paced world, efficient task scheduling is crucial. Traditional methods often struggle to keep up with complex workflows, especially when multiple processors are involved. Quantum Workflow leverages the power of quantum algorithms to provide an advanced solution for scheduling tasks. By using a hybrid approach, we combine classical and quantum computing methods to tackle the challenges of workflow optimization.
## Features
- **Hybrid Quantum-Classical Approach**: Seamlessly integrates classical algorithms with quantum techniques.
- **Efficient Resource Allocation**: Optimizes multi-processor use through smart scheduling.
- **Visual Tools**: Advanced visualization tools help you understand and manage workflows.
- **QUBO Problem Formulation**: Ensures efficient and effective solutions to scheduling problems.
- **Open Source**: Community-driven project with contributions welcome.
## Getting Started
To get started with Quantum Workflow, follow these steps to set up your environment and run the project.
### Prerequisites
Make sure you have the following installed:
- Python 3.7 or higher
- Qiskit
- Required libraries (see Installation section)
### Installation
1. Clone the repository:
```bash
git clone https://github.com/meet-c-viradiya/quantum-workflow.git
cd quantum-workflow
```
2. Install the required packages:
```bash
pip install -r requirements.txt
```
### Usage
Once installed, you can start using Quantum Workflow for your scheduling needs. The main script is located in the `src` directory.
Run the main script:
```bash
python src/main.py
```
### Example
To see how the Quantum Workflow can be applied, check the examples provided in the `examples` directory. Here’s a simple way to visualize a task scheduling scenario:
```python
from quantum_workflow import Scheduler
scheduler = Scheduler()
scheduler.add_task("Task 1", duration=2)
scheduler.add_task("Task 2", duration=3)
scheduler.schedule()
```
This code snippet demonstrates how to create a scheduler, add tasks, and schedule them efficiently.
## Contributing
We welcome contributions from everyone. To contribute:
1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes and commit them (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a pull request.
Your contributions help improve the project and make it more useful for everyone.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Contact
For questions or feedback, feel free to reach out:
- Email: your-email@example.com
- GitHub: [meet-c-viradiya](https://github.com/meet-c-viradiya)
## Releases
To download the latest release, visit the [Releases section](https://github.com/meet-c-viradiya/quantum-workflow/releases). Download the appropriate files and execute them to get started with the latest features and improvements.
---
Thank you for checking out Quantum Workflow! We hope you find it useful for your quantum computing and scheduling needs. If you have any questions or suggestions, please reach out through the contact information provided. Happy coding!