semvec/pypi-packages huggingface dataset:
QuBayes 0.1.0
Python package for performing quantum Bayesian inference
License: Apache-2.0
Requires Python: >=3.12

# QuBayes

QuBayes is a Python package for performing quantum Bayesian inference using Qiskit. It provides a framework for representing Bayesian networks, converting them into quantum circuits, and applying them to real-world problems such as chip yield analysis.

## Features

- Bayesian network representation and manipulation
  - Create, modify, and analyze Bayesian networks
  - Calculate node degrees and perform topological sorting
  - Perform simple inference using variable elimination
- Conversion of Bayesian networks to quantum circuits using Qiskit
  - Map nodes to qubits and conditional probabilities to qubit states
  - Generate efficient quantum circuits ready for visualization and verification
- Inference on quantum circuits for probabilistic reasoning
  - Perform inference tasks on the generated quantum circuits
  - Interpret and visualize results for insights into the modeled problem
- Application to real-world problems like chip yield analysis
  - Easily adapt QuBayes to analyze chip fabrication data and predict yields
  - Extensible design allows for accommodating different types of chip data and analysis needs
- Modular and extensible design for easy adaptation to other domains
  - Well-structured codebase following object-oriented design principles
  - Documented and tested for reliability and ease of use

## Installation

To install QuBayes, simply run:
```
pip install qubayes
```


## Usage

Here's a simple example of how to use QuBayes to create a Bayesian network and convert it to a quantum circuit:

```python
from qubayes.bayesnet import BayesNet
from qubayes.quantum import QuantumBayesNet

# Create a Bayesian network
bn = BayesNet()
bn.add_node('A')
bn.add_node('B')
bn.add_edge('A', 'B')

# Convert to quantum circuit
qbn = QuantumBayesNet(bn)
qc = qbn.to_circuit()
Visualize the circuit
qc.draw()
```

(Not implemented yet) For more detailed examples and tutorials, please see the [Documentation](https://qubayes.readthedocs.io/).



## Acknowledgements

QuBayes is released under the [Apache License 2.0](LICENSE).

If you use QuBayes in your research, please cite it as follows:

```bibtex
@misc{QuBayes,
author = {Zi-ming Li, Zeji Li},
title = {QuBayes: A Python Package for Quantum Bayesian Inference},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/BaksiLi/QuBayes}},
primaryClass = {quant-ph}
}
```

(Not implemented yet) Contributions to QuBayes are welcome! Please see the [Contributing Guide](CONTRIBUTING.md) for guidelines on how to contribute code, documentation, or bug reports.