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))
Donate
You can donate for this project!
ETH - ERC20: 0xa6F7170Ca63cf284A8ba6339b565445468E04Ff2
BTC - Bech32: bc1qfek2lun4tc7d7zftz0v4auxc9dzn77h9xq9x26v02u6s3rgl7hesxt4r2h
USDT - TRC20: TWVcF24DjPnGfhegmJjBQw2iE4vxQBuYTY
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
Built Distribution
File details
Details for the file quantum6g-1.1.2-py3-none-any.whl
.
File metadata
- Download URL: quantum6g-1.1.2-py3-none-any.whl
- Upload date:
- Size: 3.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.1
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0acab613d859b0f72f8b5d93fa70c3ad14d570910adee28c96cd86df0b8ca409 |
|
MD5 | 76bf2456449532068e43eccffed7ebf7 |
|
BLAKE2b-256 | 61226c03eace4c511f55f630ad7fb985fc581d871404742488847e9ca9a4d424 |