Skip to main content

This library is an automatic artificial intelligence library that combines Quantum and 6G technologies.

Project description

Quantum6G: Auto AI Advanced Quantum Neural Networks with 6G Technology

Quantum6G is an automatic artificial intelligence library that combines quantum computing and 6G technologies to build advanced quantum neural networks. It provides a high-level interface for constructing, training, and evaluating quantum neural networks. This library was developed by Emirhan BULUT.

Installation

To install the Quantum6G library, simply run the following command:

pip install quantum6g

Getting Started

Here is a simple example to get started with the Quantum6G library:

from quantum6g import Quantum6G
import numpy 

Create a quantum neural network

quantum_6g = Quantum6G()

Build the model

quantum_6g_model = quantum_6g.build_model(X_train, y_train, X_test, y_test)

Evaluate the model

accuracy = quantum_6g_model.evaluate(X_test,y_test)[1]
print("Accuracy: {:.2f}%".format(accuracy * 100)) 

Documentation

For more information on how to use the Quantum6G library, please refer to the documentation available at [the soon].

Contributing

We welcome contributions to the Quantum6G library. If you would like to contribute, please fork the repository and make your changes, then submit a pull request.

License

The Quantum6G library is open source and released under the MIT license. For more information, please see the LICENSE file.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

quantum6g-1.1.1-py3-none-any.whl (3.5 kB view details)

Uploaded Python 3

File details

Details for the file quantum6g-1.1.1-py3-none-any.whl.

File metadata

  • Download URL: quantum6g-1.1.1-py3-none-any.whl
  • Upload date:
  • Size: 3.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.1

File hashes

Hashes for quantum6g-1.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 99630c6de92080a4cd04ba1557f1ebad8384335f6ed49173adfe633d9aa4b445
MD5 ec9559402a1a2e13b453bfa494940dc9
BLAKE2b-256 5ade01d04cc51675ebca9ff423736bb914567264b3ec0f326b11956134e0d307

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page