🧬 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.

Getting Started 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. License: Receive and activate your license file

import quantumlangchain as qlc

# Display licensing information
qlc.display_license_info()

# Get your machine ID for licensing
machine_id = qlc.get_machine_id()
print(f"Machine ID: {machine_id}")

🌟 What is QuantumLangChain?¶

QuantumLangChain bridges the gap between classical AI and quantum computing, providing a unified framework for building hybrid quantum-classical AI systems. It brings quantum-inspired concepts like entanglement, superposition, and decoherence to traditional AI workflows, enabling new forms of reasoning and collaboration.

🔬 Key Innovations¶

• 🔗 Quantum-Classical Hybridization: Seamlessly blend quantum algorithms with classical AI
• 🧠 Entangled Memory Systems: Memory that maintains quantum correlations across operations
• 🤖 Multi-Agent Quantum Collaboration: Agents that share quantum belief states
• ⚡ Quantum-Enhanced Retrieval: Grover-inspired search for semantic similarity
• 🔄 Reversible Operations: Timeline manipulation and state rollback capabilities
• 🛡️ Quantum Error Correction: Built-in decoherence management and error correction

🏗️ Architecture Overview¶

graph TB
    subgraph "Application Layer"
        A[User Applications]
        B[CLI Tools]
        C[Jupyter Notebooks]
    end

    subgraph "Core Framework"
        D[QLChain]
        E[EntangledAgents]
        F[QuantumMemory]
        G[QuantumRetriever]
    end

    subgraph "Quantum Backends"
        H[Qiskit]
        I[PennyLane]
        J[Braket]
        K[Cirq]
    end

    subgraph "Storage Layer"
        L[HybridChromaDB]
        M[QuantumFAISS]
        N[Classical DBs]
    end

    A --> D
    B --> E
    C --> F
    D --> H
    E --> I
    F --> J
    G --> K
    F --> L
    G --> M
    D --> N

🚀 Quick Start¶

Installation¶

# Basic installation
pip install quantumlangchain

# With all optional dependencies
pip install quantumlangchain[all]

# Development installation
git clone https://github.com/krish567366/Quantum-Langchain.git
cd Quantum-Langchain
pip install -e .[dev]

Basic Quantum Chain¶

import asyncio
from quantumlangchain import QLChain, QuantumMemory, QiskitBackend

async def main():
    # Initialize quantum backend
    backend = QiskitBackend()

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

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

    # Initialize and run
    await chain.initialize()
    result = await chain.arun("Analyze quantum implications of AI alignment")

    print(f"Result: {result}")
    print(f"Quantum State: {chain.quantum_state}")
    print(f"Decoherence: {chain.decoherence_level:.3f}")

asyncio.run(main())

Multi-Agent Entanglement¶

from quantumlangchain import EntangledAgents, SharedQuantumMemory

async def collaborative_example():
    # 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
    )

    await agents.initialize()

    # Collaborative problem solving
    solution = await agents.collaborative_solve(
        "Design a quantum machine learning algorithm",
        collaboration_type="consensus"
    )

    print(f"Collaborative Solution: {solution}")

    # Check system status
    status = await agents.get_system_status()
    print(f"Active Entanglements: {status['total_agents']}")

asyncio.run(collaborative_example())

Quantum-Enhanced Retrieval¶

from quantumlangchain import QuantumRetriever, HybridChromaDB

async def retrieval_example():
    # 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
    )

    await retriever.initialize()

    # Enhanced semantic search
    docs = await retriever.aretrieve(
        "quantum machine learning applications",
        top_k=5,
        quantum_enhanced=True
    )

    for doc in docs:
        print(f"Score: {doc['quantum_score']:.3f}")
        print(f"Content: {doc['content'][:100]}...")
        print(f"Quantum Enhanced: {doc['quantum_enhanced']}")
        print("---")

asyncio.run(retrieval_example())

🧪 Examples¶

Interactive Demos¶

# Run comprehensive demo
qlchain demo --full

# Demo specific components
qlchain demo --chain "analyze quantum computing trends"
qlchain demo --agents "optimize neural network architecture"
qlchain demo --memory
qlchain demo --retriever "quantum algorithms"

Jupyter Notebooks¶

Explore our comprehensive example notebooks:

• Basic Quantum Reasoning - Introduction to QLChain
• Memory Entanglement - Quantum memory operations
• Multi-Agent Systems - Collaborative AI agents
• Quantum RAG System - Enhanced retrieval-augmented generation
• Advanced Concepts - Deep theoretical foundations

🧬 Core Modules¶

🔗 QLChain¶

Quantum-ready chains with decoherence-aware control flows and circuit injection. Enable composable hybrid quantum-classical reasoning with superposition of execution paths.

Key Features:

• Parallel quantum branches with interference
• Decoherence-aware error correction
• Circuit injection for quantum enhancement
• Adaptive depth control

🧠 QuantumMemory¶

Reversible, entangled memory layers with hybrid vector store support. Provides quantum error correction and time-mutable embeddings.

Key Features:

• Entangled memory entries
• Reversible operations
• Quantum-enhanced similarity search
• Memory snapshots and rollback

🤖 EntangledAgents¶

Multi-agent systems with shared memory entanglement and interference-based reasoning. Enables quantum-style collaboration and belief propagation.

Key Features:

• Shared belief states
• Quantum interference between agent solutions
• Collaborative consensus building
• Swarm intelligence emergence

🔍 QuantumRetriever¶

Quantum-enhanced semantic retrieval using Grover-based subquery refinement and amplitude amplification.

Key Features:

• Grover search speedup
• Quantum similarity amplification
• Hybrid classical-quantum fallback
• Reversible attention indexing

🛠️ Supported Quantum Backends¶

📊 Performance Benchmarks¶

Benchmarks on quantum simulators with 16 qubits, averaged over 1000 runs¶

🌐 Integrations¶

🔗 LangChain Compatibility¶

from quantumlangchain.integrations import LangChainQuantumBridge
from langchain.chains import LLMChain

# Bridge quantum and classical chains
bridge = LangChainQuantumBridge()
hybrid_chain = bridge.create_hybrid_chain(
    classical_chain=LLMChain(...),
    quantum_chain=QLChain(...)
)

🤗 HuggingFace Models¶

from quantumlangchain.integrations import HuggingFaceQuantumWrapper
from transformers import AutoModel

# Quantum-enhance transformer models
model = AutoModel.from_pretrained("bert-base-uncased")
quantum_model = HuggingFaceQuantumWrapper(
    model=model,
    quantum_layers=["attention", "feedforward"]
)

🔥 Production Deployment¶

from quantumlangchain.deployment import QuantumCluster

# Deploy quantum-enhanced services
cluster = QuantumCluster(
    backend="qiskit_cloud",
    auto_scaling=True,
    error_correction=True
)

await cluster.deploy_service(chain)

🔮 Advanced Features¶

Timeline Rewriting¶

# Create memory snapshots for rollback
snapshot_id = await memory.create_memory_snapshot()

# Execute reasoning with potential rollback
result = await chain.arun("risky_operation")

if not satisfactory(result):
    # Rollback to previous state
    await memory.restore_memory_snapshot(snapshot_id)

Quantum Error Correction¶

# Configure automatic error correction
chain = QLChain(
    error_correction_threshold=0.8,
    quantum_error_correction=True,
    decoherence_mitigation="active"
)

Belief State Propagation¶

# Agents automatically share belief states
agent_1.belief_state.beliefs["task_confidence"] = 0.9

# Belief propagates through entanglement
await agents.propagate_belief_states()

# Other agents' beliefs are updated
print(agent_2.belief_state.beliefs["task_confidence"])  # ~0.75

🤝 Contributing¶

We welcome contributions! Please contact bajpaikrishna715@gmail.com for collaboration opportunities.

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/ -v
pytest tests/ -m "not slow"  # Skip slow tests
pytest tests/ -m quantum     # Only quantum tests

Code Quality¶

black quantumlangchain/
ruff check quantumlangchain/
mypy quantumlangchain/

📄 License¶

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

🙏 Acknowledgments¶

• LangChain Team - For the inspiration and composable AI architecture
• Quantum Computing Community - For advancing the field of quantum algorithms
• Open Source Contributors - For making this project possible

📞 Contact & Support¶

• Author: Krishna Bajpai
• Email: bajpaikrishna715@gmail.com
• GitHub: @krish567366
• Documentation: krish567366.github.io/Quantum-Langchain
• Issues: GitHub Issues