https://github.com/subatomicerror/quantum-ml
Quantum-ML is a hybrid quantum-classical machine learning project leveraging quantum computing and AI to deliver advanced predictions and solutions through a fast API interface.
https://github.com/subatomicerror/quantum-ml
artificial-intelligence fastapi machine-learning pennylane python pytorch quantum-ai quantum-computing quantum-machine-learning
Last synced: 4 months ago
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Quantum-ML is a hybrid quantum-classical machine learning project leveraging quantum computing and AI to deliver advanced predictions and solutions through a fast API interface.
- Host: GitHub
- URL: https://github.com/subatomicerror/quantum-ml
- Owner: subatomicERROR
- License: mit
- Created: 2024-11-24T18:01:10.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-03-07T03:14:06.000Z (7 months ago)
- Last Synced: 2025-06-15T02:08:44.946Z (4 months ago)
- Topics: artificial-intelligence, fastapi, machine-learning, pennylane, python, pytorch, quantum-ai, quantum-computing, quantum-machine-learning
- Language: Python
- Homepage:
- Size: 37.1 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Quantum-ML
Quantum-ML is a cutting-edge framework built to integrate quantum computing with machine learning, enabling high-performance quantum tasks directly through an API. Leveraging the power of **Quantum-API**, built with **FastAPI**, Quantum-ML offers a seamless, efficient backend for quantum computations, ensuring quick response times, scalability, and easy integration.
## Key Features
- **Quantum Computing Integration**: Easily run quantum circuits and computations via a fast and efficient REST API.
- **FastAPI Backend**: Quantum-API, powered by FastAPI, provides a high-performance backend for managing quantum tasks, offering minimal latency and scalability.
- **Quantum Task Management**: Efficiently manage quantum tasks and machine learning workflows, powered by PennyLane and Python.
- **Scalable Architecture**: The Quantum-ML framework is designed to scale efficiently with increasing workload and complexity.
- **Simple Deployment**: Get started with minimal setup and deploy on your preferred environment with ease.
## Why Choose Quantum-ML?
- **Optimized for Quantum Machine Learning**: Quantum-ML is uniquely designed to integrate quantum computing seamlessly into machine learning tasks. With a focus on optimizing `x` (input) for quantum circuits, it delivers faster, more accurate results.
- **Easy to Use**: The `Quantum-API` provides a user-friendly REST interface to interact with quantum algorithms and models.
- **Scalable and Efficient**: Built on FastAPI, Quantum-ML ensures that quantum tasks are executed quickly and can be scaled as needed, making it suitable for both small and large quantum machine learning workflows.
- **Seamless Integration**: Quantum-ML's API allows easy integration with other services and applications, making it ideal for modern AI and machine learning platforms.
- **State-of-the-art Quantum Algorithms**: Powered by **PennyLane**, Quantum-ML uses the latest quantum algorithms for machine learning, ensuring cutting-edge performance and results.
## How Quantum-API Powers Quantum-ML
- The **Quantum-API** provides a robust backend for quantum tasks, replacing the original FastAPI setup with a focus on quantum computing and machine learning.
- It enables quick execution of quantum circuits, managing parameters dynamically, and providing the results via a REST API interface.
- With Quantum-API integrated, **Quantum-ML** is now capable of managing complex quantum tasks efficiently, utilizing the full power of quantum computing in machine learning workflows.
## Quick Start
1. **Clone the Repository:**
```bash
git clone https://github.com/subatomicERROR/Quantum-ML.git
cd Quantum-ML
```
2. **Set up the Environment:**
```bash
python3 -m venv quantum-venv
source quantum-venv/bin/activate
```
3. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
4. **Run the Application:**
```bash
uvicorn quantum_api.main:app --reload
```
5. **Test the API:**
Send a POST request to `http://127.0.0.1:8000/run-quantum-task` with the required input.
```bash
curl -X POST "http://127.0.0.1:8000/run-quantum-task" -H "Content-Type: application/json" -d '{"x": 3.14}'
```
## Why Quantum-ML Is Unique
Quantum-ML stands out because it integrates quantum computing and machine learning with exceptional performance, scalability, and ease of use. It uniquely addresses the growing need for quantum-augmented AI by leveraging quantum algorithms to perform complex computations with minimal latency, ensuring you can make faster, more intelligent decisions in quantum machine learning.
## Repository Links
- **Quantum-API Repository**: [Quantum-API](https://github.com/subatomicERROR/Quantum-API.git)
- **Quantum-ML Repository**: [Quantum-ML](https://github.com/subatomicERROR/Quantum-ML.git)
---
Quantum-ML is an ideal choice for anyone looking to harness the power of quantum computing in machine learning, offering professional-grade performance and ease of integration into existing workflows.