probabilistic-quantum-reasoner


Nameprobabilistic-quantum-reasoner JSON
Version 1.0.0 PyPI version JSON
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home_pagehttps://github.com/quantum-ai/probabilistic-quantum-reasoner
SummaryA quantum-classical hybrid reasoning engine for uncertainty-aware AI inference
upload_time2025-07-16 04:22:22
maintainerNone
docs_urlNone
authorQuantum AI Research Team
requires_python>=3.10
licenseCOmmercial
keywords quantum bayesian reasoning ai inference probabilistic causal uncertainty quantum-computing
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            # Probabilistic Quantum Reasoner

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A **quantum-classical hybrid reasoning engine** for uncertainty-aware AI inference, fusing quantum probabilistic graphical models (QPGMs) with classical probabilistic logic.

## ๐ŸŽฏ Overview

The Probabilistic Quantum Reasoner implements a novel approach to AI reasoning by encoding knowledge using **quantum amplitude distributions** over Hilbert space, modeling uncertainty through entanglement and non-commutative conditional graphs, and enabling hybrid **Quantum Bayesian Networks** with causal, counterfactual, and abductive reasoning capabilities.

## ๐Ÿงฉ Key Features

- **Quantum Bayesian Networks**: Hybrid classical-quantum probabilistic graphical models
- **Quantum Belief Propagation**: Unitary message passing with amplitude-weighted inference
- **Causal Quantum Reasoning**: Do-calculus analog for quantum intervention logic
- **Multiple Backends**: Support for Qiskit, PennyLane, and classical simulation
- **Uncertainty Modeling**: Entanglement-based uncertainty representation
- **Counterfactual Reasoning**: Quantum counterfactuals using unitary interventions

## ๐Ÿš€ Quick Start

### Installation

```bash
pip install probabilistic-quantum-reasoner
```

For development with extra features:

```bash
pip install probabilistic-quantum-reasoner[dev,docs,extras]
```

### Basic Usage

```python
from probabilistic_quantum_reasoner import QuantumBayesianNetwork
from probabilistic_quantum_reasoner.backends import QiskitBackend

# Create a quantum Bayesian network
qbn = QuantumBayesianNetwork(backend=QiskitBackend())

# Add quantum and classical nodes
weather = qbn.add_quantum_node("weather", ["sunny", "rainy"])
mood = qbn.add_stochastic_node("mood", ["happy", "sad"])

# Create entangled relationship
qbn.add_edge(weather, mood)
qbn.entangle([weather, mood])

# Perform quantum inference
result = qbn.infer(evidence={"weather": "sunny"})
print(f"Mood probabilities: {result}")

# Quantum intervention (do-calculus)
intervention_result = qbn.intervene("weather", "rainy")
print(f"Mood under intervention: {intervention_result}")
```

## ๐Ÿงฌ Mathematical Foundation

The library implements quantum probabilistic reasoning using:

- **Tensor Product Spaces**: Joint state representation as |ฯˆโŸฉ = ฮฃแตขโฑผ ฮฑแตขโฑผ|iโฑผโŸฉ
- **Amplitude Manipulation**: Via Kraus operators and parameterized unitaries
- **Density Matrix Operations**: Mixed state inference through partial tracing
- **Non-commutative Conditional Probability**: P_Q(A|B) โ‰  P_Q(B|A) in general

## ๐Ÿ“– Documentation

- **[API Reference](https://krish567366.github.io/probabilistic-quantum-reasoner/api-reference/)**
- **[Architecture Guide](https://krish567366.github.io/probabilistic-quantum-reasoner/architecture/)**
- **[Examples & Tutorials](https://krish567366.github.io/probabilistic-quantum-reasoner/examples/)**

## ๐Ÿงช Examples

### Quantum XOR Reasoning
```python
# Create entangled XOR gate reasoning
qbn = QuantumBayesianNetwork()
a = qbn.add_quantum_node("A", [0, 1])
b = qbn.add_quantum_node("B", [0, 1])
xor = qbn.add_quantum_node("XOR", [0, 1])

qbn.add_quantum_xor_relationship(a, b, xor)
result = qbn.infer(evidence={"A": 1, "B": 0})
```

### Weather-Mood Causal Graph
```python
# Hybrid classical-quantum causal modeling
from probabilistic_quantum_reasoner.examples import WeatherMoodExample

example = WeatherMoodExample()
causal_effect = example.estimate_causal_effect("weather", "mood")
counterfactual = example.counterfactual_query("What if it was sunny?")
```

## ๐Ÿ› ๏ธ Architecture

```
probabilistic_quantum_reasoner/
โ”œโ”€โ”€ core/                    # Core network structures
โ”‚   โ”œโ”€โ”€ network.py          # QuantumBayesianNetwork
โ”‚   โ”œโ”€โ”€ nodes.py            # Quantum/Stochastic/Hybrid nodes
โ”‚   โ””โ”€โ”€ operators.py        # Quantum operators and gates
โ”œโ”€โ”€ inference/              # Reasoning engines
โ”‚   โ”œโ”€โ”€ engine.py           # Main inference engine
โ”‚   โ”œโ”€โ”€ causal.py           # Causal reasoning
โ”‚   โ”œโ”€โ”€ belief_propagation.py
โ”‚   โ””โ”€โ”€ variational.py      # Variational quantum inference
โ”œโ”€โ”€ backends/               # Backend implementations
โ”‚   โ”œโ”€โ”€ qiskit_backend.py
โ”‚   โ”œโ”€โ”€ pennylane_backend.py
โ”‚   โ””โ”€โ”€ simulator.py
โ””โ”€โ”€ examples/               # Example implementations
```

## ๐Ÿ”ฌ Research Applications

- **AGI Inference Scaffolds**: Uncertainty-aware reasoning for autonomous systems
- **Quantum Explainable AI (Q-XAI)**: Interpretable quantum decision making
- **Counterfactual Analysis**: "What-if" scenarios in quantum superposition
- **Epistemic Uncertainty Modeling**: Non-classical uncertainty representation

## ๐Ÿค Contributing

We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## ๐Ÿ“ License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## ๐Ÿ“š Citation

If you use this library in your research, please cite:

```bibtex
@software{bajpai2025quantum,
  title={Probabilistic Quantum Reasoner: A Hybrid Quantum-Classical Reasoning Engine},
  author={Bajpai, Krishna},
  year={2025},
  url={https://github.com/krish567366/probabilistic-quantum-reasoner}
}
```

## ๐Ÿ‘จโ€๐Ÿ’ป Author

**Krishna Bajpai**
- Email: bajpaikrishna715@gmail.com
- GitHub: [@krish567366](https://github.com/krish567366)

## ๐Ÿ™ Acknowledgments

- Quantum computing community for foundational algorithms
- Classical probabilistic reasoning research
- Open source quantum computing frameworks (Qiskit, PennyLane)

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    "description": "# Probabilistic Quantum Reasoner\r\n\r\n[![PyPI](https://img.shields.io/pypi/v/probabilistic-quantum-reasoner.svg?label=PyPI&color=purple&logo=python&logoColor=white)](https://pypi.org/project/probabilistic-quantum-reasoner/)\r\n[![PyPI Downloads](https://static.pepy.tech/badge/probabilistic-quantum-reasoner)](https://pepy.tech/projects/probabilistic-quantum-reasoner)\r\n[![Docs](https://img.shields.io/badge/docs-online-blue?logo=readthedocs)](https://krish567366.github.io/probabilistic-quantum-reasoner/)\r\n[![License: Commercial](https://img.shields.io/badge/license-commercial-blueviolet?logo=briefcase)](https://krish567366.github.io/license-server/)\r\n[![Python 3.10+](https://img.shields.io/badge/python-3.10+-black.svg)](https://www.python.org/downloads/)\r\n\r\nA **quantum-classical hybrid reasoning engine** for uncertainty-aware AI inference, fusing quantum probabilistic graphical models (QPGMs) with classical probabilistic logic.\r\n\r\n## \ud83c\udfaf Overview\r\n\r\nThe Probabilistic Quantum Reasoner implements a novel approach to AI reasoning by encoding knowledge using **quantum amplitude distributions** over Hilbert space, modeling uncertainty through entanglement and non-commutative conditional graphs, and enabling hybrid **Quantum Bayesian Networks** with causal, counterfactual, and abductive reasoning capabilities.\r\n\r\n## \ud83e\udde9 Key Features\r\n\r\n- **Quantum Bayesian Networks**: Hybrid classical-quantum probabilistic graphical models\r\n- **Quantum Belief Propagation**: Unitary message passing with amplitude-weighted inference\r\n- **Causal Quantum Reasoning**: Do-calculus analog for quantum intervention logic\r\n- **Multiple Backends**: Support for Qiskit, PennyLane, and classical simulation\r\n- **Uncertainty Modeling**: Entanglement-based uncertainty representation\r\n- **Counterfactual Reasoning**: Quantum counterfactuals using unitary interventions\r\n\r\n## \ud83d\ude80 Quick Start\r\n\r\n### Installation\r\n\r\n```bash\r\npip install probabilistic-quantum-reasoner\r\n```\r\n\r\nFor development with extra features:\r\n\r\n```bash\r\npip install probabilistic-quantum-reasoner[dev,docs,extras]\r\n```\r\n\r\n### Basic Usage\r\n\r\n```python\r\nfrom probabilistic_quantum_reasoner import QuantumBayesianNetwork\r\nfrom probabilistic_quantum_reasoner.backends import QiskitBackend\r\n\r\n# Create a quantum Bayesian network\r\nqbn = QuantumBayesianNetwork(backend=QiskitBackend())\r\n\r\n# Add quantum and classical nodes\r\nweather = qbn.add_quantum_node(\"weather\", [\"sunny\", \"rainy\"])\r\nmood = qbn.add_stochastic_node(\"mood\", [\"happy\", \"sad\"])\r\n\r\n# Create entangled relationship\r\nqbn.add_edge(weather, mood)\r\nqbn.entangle([weather, mood])\r\n\r\n# Perform quantum inference\r\nresult = qbn.infer(evidence={\"weather\": \"sunny\"})\r\nprint(f\"Mood probabilities: {result}\")\r\n\r\n# Quantum intervention (do-calculus)\r\nintervention_result = qbn.intervene(\"weather\", \"rainy\")\r\nprint(f\"Mood under intervention: {intervention_result}\")\r\n```\r\n\r\n## \ud83e\uddec Mathematical Foundation\r\n\r\nThe library implements quantum probabilistic reasoning using:\r\n\r\n- **Tensor Product Spaces**: Joint state representation as |\u03c8\u27e9 = \u03a3\u1d62\u2c7c \u03b1\u1d62\u2c7c|i\u2c7c\u27e9\r\n- **Amplitude Manipulation**: Via Kraus operators and parameterized unitaries\r\n- **Density Matrix Operations**: Mixed state inference through partial tracing\r\n- **Non-commutative Conditional Probability**: P_Q(A|B) \u2260 P_Q(B|A) in general\r\n\r\n## \ud83d\udcd6 Documentation\r\n\r\n- **[API Reference](https://krish567366.github.io/probabilistic-quantum-reasoner/api-reference/)**\r\n- **[Architecture Guide](https://krish567366.github.io/probabilistic-quantum-reasoner/architecture/)**\r\n- **[Examples & Tutorials](https://krish567366.github.io/probabilistic-quantum-reasoner/examples/)**\r\n\r\n## \ud83e\uddea Examples\r\n\r\n### Quantum XOR Reasoning\r\n```python\r\n# Create entangled XOR gate reasoning\r\nqbn = QuantumBayesianNetwork()\r\na = qbn.add_quantum_node(\"A\", [0, 1])\r\nb = qbn.add_quantum_node(\"B\", [0, 1])\r\nxor = qbn.add_quantum_node(\"XOR\", [0, 1])\r\n\r\nqbn.add_quantum_xor_relationship(a, b, xor)\r\nresult = qbn.infer(evidence={\"A\": 1, \"B\": 0})\r\n```\r\n\r\n### Weather-Mood Causal Graph\r\n```python\r\n# Hybrid classical-quantum causal modeling\r\nfrom probabilistic_quantum_reasoner.examples import WeatherMoodExample\r\n\r\nexample = WeatherMoodExample()\r\ncausal_effect = example.estimate_causal_effect(\"weather\", \"mood\")\r\ncounterfactual = example.counterfactual_query(\"What if it was sunny?\")\r\n```\r\n\r\n## \ud83d\udee0\ufe0f Architecture\r\n\r\n```\r\nprobabilistic_quantum_reasoner/\r\n\u251c\u2500\u2500 core/                    # Core network structures\r\n\u2502   \u251c\u2500\u2500 network.py          # QuantumBayesianNetwork\r\n\u2502   \u251c\u2500\u2500 nodes.py            # Quantum/Stochastic/Hybrid nodes\r\n\u2502   \u2514\u2500\u2500 operators.py        # Quantum operators and gates\r\n\u251c\u2500\u2500 inference/              # Reasoning engines\r\n\u2502   \u251c\u2500\u2500 engine.py           # Main inference engine\r\n\u2502   \u251c\u2500\u2500 causal.py           # Causal reasoning\r\n\u2502   \u251c\u2500\u2500 belief_propagation.py\r\n\u2502   \u2514\u2500\u2500 variational.py      # Variational quantum inference\r\n\u251c\u2500\u2500 backends/               # Backend implementations\r\n\u2502   \u251c\u2500\u2500 qiskit_backend.py\r\n\u2502   \u251c\u2500\u2500 pennylane_backend.py\r\n\u2502   \u2514\u2500\u2500 simulator.py\r\n\u2514\u2500\u2500 examples/               # Example implementations\r\n```\r\n\r\n## \ud83d\udd2c Research Applications\r\n\r\n- **AGI Inference Scaffolds**: Uncertainty-aware reasoning for autonomous systems\r\n- **Quantum Explainable AI (Q-XAI)**: Interpretable quantum decision making\r\n- **Counterfactual Analysis**: \"What-if\" scenarios in quantum superposition\r\n- **Epistemic Uncertainty Modeling**: Non-classical uncertainty representation\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\r\n\r\n1. Fork the repository\r\n2. Create a feature branch (`git checkout -b feature/amazing-feature`)\r\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\r\n4. Push to the branch (`git push origin feature/amazing-feature`)\r\n5. 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Quantum AI Research Team
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