{"id":22202461,"url":"https://github.com/subatomicerror/quantum-ml","last_synced_at":"2025-06-15T02:08:45.317Z","repository":{"id":264564309,"uuid":"893544081","full_name":"subatomicERROR/Quantum-ML","owner":"subatomicERROR","description":"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.","archived":false,"fork":false,"pushed_at":"2025-03-07T03:14:06.000Z","size":38,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-06-15T02:08:44.946Z","etag":null,"topics":["artificial-intelligence","fastapi","machine-learning","pennylane","python","pytorch","quantum-ai","quantum-computing","quantum-machine-learning"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/subatomicERROR.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-11-24T18:01:10.000Z","updated_at":"2025-03-07T03:14:09.000Z","dependencies_parsed_at":"2025-02-03T21:41:01.074Z","dependency_job_id":"15c1a25d-2bd8-40f7-bc73-60cc73155c82","html_url":"https://github.com/subatomicERROR/Quantum-ML","commit_stats":null,"previous_names":["subatomicerror/quantum-ml"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/subatomicERROR/Quantum-ML","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/subatomicERROR%2FQuantum-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/subatomicERROR%2FQuantum-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/subatomicERROR%2FQuantum-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/subatomicERROR%2FQuantum-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/subatomicERROR","download_url":"https://codeload.github.com/subatomicERROR/Quantum-ML/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/subatomicERROR%2FQuantum-ML/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259910730,"owners_count":22930711,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","fastapi","machine-learning","pennylane","python","pytorch","quantum-ai","quantum-computing","quantum-machine-learning"],"created_at":"2024-12-02T16:25:10.164Z","updated_at":"2025-06-15T02:08:45.298Z","avatar_url":"https://github.com/subatomicERROR.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Quantum-ML\n\nQuantum-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.\n\n## Key Features\n- **Quantum Computing Integration**: Easily run quantum circuits and computations via a fast and efficient REST API.\n- **FastAPI Backend**: Quantum-API, powered by FastAPI, provides a high-performance backend for managing quantum tasks, offering minimal latency and scalability.\n- **Quantum Task Management**: Efficiently manage quantum tasks and machine learning workflows, powered by PennyLane and Python.\n- **Scalable Architecture**: The Quantum-ML framework is designed to scale efficiently with increasing workload and complexity.\n- **Simple Deployment**: Get started with minimal setup and deploy on your preferred environment with ease.\n\n## Why Choose Quantum-ML?\n- **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.\n- **Easy to Use**: The `Quantum-API` provides a user-friendly REST interface to interact with quantum algorithms and models.\n- **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.\n- **Seamless Integration**: Quantum-ML's API allows easy integration with other services and applications, making it ideal for modern AI and machine learning platforms.\n- **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.\n\n## How Quantum-API Powers Quantum-ML\n- The **Quantum-API** provides a robust backend for quantum tasks, replacing the original FastAPI setup with a focus on quantum computing and machine learning.\n- It enables quick execution of quantum circuits, managing parameters dynamically, and providing the results via a REST API interface.\n- 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.\n\n## Quick Start\n\n1. **Clone the Repository:**\n    ```bash\n    git clone https://github.com/subatomicERROR/Quantum-ML.git\n    cd Quantum-ML\n    ```\n\n2. **Set up the Environment:**\n    ```bash\n    python3 -m venv quantum-venv\n    source quantum-venv/bin/activate\n    ```\n\n3. **Install Dependencies:**\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n4. **Run the Application:**\n    ```bash\n    uvicorn quantum_api.main:app --reload\n    ```\n\n5. **Test the API:**\n    Send a POST request to `http://127.0.0.1:8000/run-quantum-task` with the required input.\n    ```bash\n    curl -X POST \"http://127.0.0.1:8000/run-quantum-task\" -H \"Content-Type: application/json\" -d '{\"x\": 3.14}'\n    ```\n\n## Why Quantum-ML Is Unique\n\nQuantum-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.\n\n## Repository Links\n\n- **Quantum-API Repository**: [Quantum-API](https://github.com/subatomicERROR/Quantum-API.git)\n- **Quantum-ML Repository**: [Quantum-ML](https://github.com/subatomicERROR/Quantum-ML.git)\n\n---\n\nQuantum-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.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsubatomicerror%2Fquantum-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsubatomicerror%2Fquantum-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsubatomicerror%2Fquantum-ml/lists"}