Metadata-Version: 2.1
Name: quantum6g
Version: 1.2.5
Summary: This library is an automatic artificial intelligence library that combines Quantum and 6G technologies.
Home-page: https://github.com/emirhanai/quantum6g
Author: Emirhan BULUT
Author-email: emirhan@isap.solutions
Keywords: quantum machine-learning,quantum 6g,6g,quantum,AI,quantum neural networks,qnn,quantum ai,artificial intelligence
Description-Content-Type: text/markdown


# 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](https://linkedin.com/in/aiemir).

## 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
```

## Create a quantum neural network

``` quantum_6g = Quantum6G(output_unit=1, num_layers=4, epochs=2, loss='mse', input=4, batch_size=256, learning_rate=0.2)```

## Build the model
``` 
quantum_6g = quantum_6g.build_model(X_train, y_train, X_test, y_test)
```

## Evaluate the model
``` 
print("Accuracy: {:.2f}%".format(quantum_6g[1][1] * 100))
print("Loss: {:.2f}%".format(quantum_6g[1][0] * 100))
```

## Build and Fit Quantum6G_KNN --- from v1.2.5V
``` 
quantum_knn = Quantum6G_KNN(n_qubits=4, n_neighbors=6)
quantum_knn.fit(X_train, y_train)
```
## Evaluate the Quantum6G_KNN model
``` 
quantum_pred = quantum_knn.predict(X_test,y_test)
quantum_accuracy = accuracy_score(y_test, quantum_pred)
print(f"Accuracy of Quantum6G_KNN: {quantum_accuracy:.3f}")
```

## Donate
You can donate for this project!

**ETH - ERC20**: 0xa6F7170Ca63cf284A8ba6339b565445468E04Ff2

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## 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](https://choosealicense.com/licenses/mit/) file.

