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.
- 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.
- 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-APIprovides 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.
- 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.
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Clone the Repository:
git clone https://github.com/subatomicERROR/Quantum-ML.git cd Quantum-ML -
Set up the Environment:
python3 -m venv quantum-venv source quantum-venv/bin/activate -
Install Dependencies:
pip install -r requirements.txt
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Run the Application:
uvicorn quantum_api.main:app --reload
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Test the API: Send a POST request to
http://127.0.0.1:8000/run-quantum-taskwith the required input.curl -X POST "http://127.0.0.1:8000/run-quantum-task" -H "Content-Type: application/json" -d '{"x": 3.14}'
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.
- Quantum-API Repository: Quantum-API
- Quantum-ML Repository: Quantum-ML
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.