🧬 QuantumLangChain

LICENSED SOFTWARE: A composable framework for quantum-inspired reasoning, entangled memory systems, and multi-agent cooperation — engineered for next-gen artificial intelligence.

📧 Contact: bajpaikrishna715@gmail.com for licensing
⏰ 24-hour grace period available for evaluation

Licensing

⚠️ IMPORTANT: QuantumLangChain is commercial software requiring a valid license for all features beyond the 24-hour evaluation period.

Quick Start with Licensing

1. Install: pip install quantumlangchain
2. Import: Automatically starts 24-hour evaluation
3. Get Machine ID: python -c "import quantumlangchain; print(quantumlangchain.get_machine_id())"
4. Contact: Email bajpaikrishna715@gmail.com with your machine ID
5. Activate: Receive and activate your license file

🧬 About QuantumLangChain

QuantumLangChain bridges the gap between classical AI and quantum computing, providing a unified framework for building hybrid quantum-classical AI systems with advanced memory management, multi-agent cooperation, and quantum-inspired reasoning capabilities.

🚀 Features

🔧 Core Modules

• QLChain: Quantum-ready chains with decoherence-aware control flows and circuit injection
• QuantumMemory: Reversible, entangled memory layers with hybrid vector store support
• QuantumToolExecutor: Tool execution router with quantum-classical API bridge
• EntangledAgents: Multi-agent systems with shared memory entanglement and interference-based reasoning
• QPromptChain: Prompt chaining with quantum-style uncertainty branching
• QuantumRetriever: Quantum-enhanced semantic retrieval using Grover-based subquery refinement
• QuantumContextManager: Temporal snapshots and dynamic context expansion

🧬 Advanced Capabilities

• Decoherence-Aware Reasoning: Simulate quantum noise impact on logic and decision trees
• Timeline Rewriting: Memory snapshotting, branching, and rollback of reasoning paths
• Entangled Collaboration: Agents with shared belief states and quantum-style communication
• Self-Adaptive Reasoning Graphs: Dynamic agent chain restructuring during execution

📦 Installation

Basic Installation

pip install quantumlangchain

Development Installation

pip install quantumlangchain[dev]

Full Installation (with all optional dependencies)

pip install quantumlangchain[all]

From Source

git clone https://github.com/krish567366/Quantum-Langchain.git
cd Quantum-Langchain
pip install -e .

🧠 Quick Start

Basic Quantum Chain

from quantumlangchain import QLChain, QuantumMemory
from quantumlangchain.backends import QiskitBackend

# Initialize quantum backend
backend = QiskitBackend()

# Create quantum memory
memory = QuantumMemory(
    classical_dim=512,
    quantum_dim=8,
    backend=backend
)

# Build a quantum chain
chain = QLChain(
    memory=memory,
    decoherence_threshold=0.1,
    circuit_depth=10
)

# Execute with quantum-classical hybrid reasoning
result = await chain.arun("Analyze the quantum implications of this dataset")

Multi-Agent Entanglement

from quantumlangchain import EntangledAgents, SharedQuantumMemory

# Create shared quantum memory
shared_memory = SharedQuantumMemory(agents=3, entanglement_depth=4)

# Initialize entangled agents
agents = EntangledAgents(
    agent_count=3,
    shared_memory=shared_memory,
    interference_weight=0.3
)

# Collaborative quantum reasoning
results = await agents.collaborative_solve(
    "Complex multi-dimensional optimization problem"
)

Quantum-Enhanced Retrieval

from quantumlangchain import QuantumRetriever
from quantumlangchain.vectorstores import HybridChromaDB

# Setup hybrid vector store
vectorstore = HybridChromaDB(
    classical_embeddings=True,
    quantum_embeddings=True,
    entanglement_degree=2
)

# Quantum retriever with Grover enhancement
retriever = QuantumRetriever(
    vectorstore=vectorstore,
    grover_iterations=3,
    quantum_speedup=True
)

# Enhanced semantic search
docs = await retriever.aretrieve("quantum machine learning applications")

🛠️ Supported Quantum Backends

• Qiskit: IBM Quantum platform integration
• PennyLane: Differentiable quantum programming
• Amazon Braket: AWS quantum computing service
• Cirq: Google's quantum computing framework
• Qulacs: High-performance quantum simulator

📚 Documentation

Comprehensive documentation is available at krish567366.github.io/Quantum-Langchain

Key Sections

• Getting Started Guide
• API Reference
• Quantum Concepts
• Integration Tutorials
• Examples Gallery

🧪 Examples

Check out our comprehensive examples in the /examples directory:

• Basic Quantum Reasoning: examples/basic_quantum_chain.ipynb
• Memory Entanglement: examples/quantum_memory_demo.ipynb
• Multi-Agent Systems: examples/entangled_agents.ipynb
• Quantum Retrieval: examples/quantum_rag_system.ipynb
• Timeline Manipulation: examples/temporal_reasoning.ipynb

🧬 Architecture

QuantumLangChain follows a modular, extensible architecture:

quantumlangchain/
├── core/           # Core quantum-classical interfaces
├── chains/         # QLChain implementations
├── memory/         # Quantum memory systems
├── agents/         # Entangled agent frameworks
├── tools/          # Quantum tool executors
├── retrievers/     # Quantum-enhanced retrieval
├── backends/       # Quantum backend abstractions
├── vectorstores/   # Hybrid vector databases
└── utils/          # Utility functions and helpers

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/krish567366/Quantum-Langchain.git
cd Quantum-Langchain
pip install -e .[dev]
pre-commit install

Running Tests

pytest tests/

Code Formatting

black quantumlangchain/
ruff check quantumlangchain/

📊 Performance Benchmarks

Benchmarks run on quantum simulators with 16 qubits

🔮 Roadmap

• Q1 2025: Hardware quantum backend integration
• Q2 2025: Advanced error correction protocols
• Q3 2025: Quantum neural network support
• Q4 2025: Distributed quantum computing

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Inspired by LangChain's composable AI architecture
• Built on the shoulders of giants in quantum computing
• Special thanks to the quantum computing research community

📞 Contact

Krishna Bajpai

• Email: bajpaikrishna715@gmail.com
• GitHub: @krish567366
• Project: Quantum-Langchain

"Bridging the quantum-classical divide in artificial intelligence" 🌉⚛️🤖