🔍 Quantum Retriever Module¶

🔐 Licensed Component - Contact: bajpaikrishna715@gmail.com for licensing

Quantum Retriever Architecture¶

graph TB
    subgraph "Retrieval Pipeline"
        A[Query Processing]
        B[Quantum Encoding]
        C[Search Execution]
        D[Result Processing]
    end

    subgraph "Quantum Search Algorithms"
        E[Grover's Algorithm]
        F[Amplitude Amplification]
        G[Quantum Walk Search]
        H[Hybrid Search]
    end

    subgraph "Vector Store Integration"
        I[Classical Vector Stores]
        J[Quantum Vector Stores]
        K[Hybrid Stores]
        L[Distributed Stores]
    end

    subgraph "Performance Optimization"
        M[Query Optimization]
        N[Cache Management]
        O[Parallel Processing]
        P[Result Ranking]
    end

    A --> E
    B --> F
    C --> G
    D --> H

    E --> I
    F --> J
    G --> K
    H --> L

    I --> M
    J --> N
    K --> O
    L --> P

🌟 Core Features¶

Quantum-Enhanced Search¶

graph LR
    subgraph "Classical Search"
        A[Linear Search]
        B[Binary Search]
        C[Hash Lookup]
        D[O(n) or O(log n)]
    end

    subgraph "Quantum Search"
        E[Grover Search]
        F[Amplitude Amplification]
        G[Quantum Interference]
        H[O(sqrt(n))]
    end

    subgraph "Hybrid Benefits"
        I[Best Performance]
        J[Adaptive Selection]
        K[Error Resilience]
        L[Scalable Solutions]
    end

    A --> I
    B --> J
    C --> K
    D --> L

    E --> I
    F --> J
    G --> K
    H --> L

🔧 Implementation¶

Basic Quantum Retriever¶

from quantumlangchain.retrievers import QuantumRetriever
from quantumlangchain.vectorstores import QuantumVectorStore

# Initialize quantum retriever
retriever = QuantumRetriever(
    vectorstore=QuantumVectorStore(),
    search_algorithm="grover",
    quantum_dim=8,
    k_results=10
)

# Quantum-enhanced retrieval
docs = await retriever.aget_relevant_documents(
    "quantum computing applications",
    search_kwargs={"quantum_interference": True}
)

Advanced Multi-Modal Retriever¶

from quantumlangchain.retrievers import MultiModalQuantumRetriever

class AdvancedRetriever:
    def __init__(self):
        self.retriever = MultiModalQuantumRetriever(
            text_vectorstore=text_store,
            image_vectorstore=image_store,
            audio_vectorstore=audio_store,
            cross_modal_entanglement=True
        )

    async def quantum_multimodal_search(self, query):
        """Search across multiple modalities with quantum enhancement."""
        results = await self.retriever.quantum_search(
            query,
            modalities=["text", "image", "audio"],
            entanglement_strength=0.9
        )
        return results

🎯 Search Algorithms¶

Grover's Search Implementation¶

graph TB
    subgraph "Grover's Algorithm"
        A[Initialize Superposition]
        B[Oracle Function]
        C[Diffusion Operator]
        D[Iterate sqrt(N) times]
    end

    subgraph "Quantum Advantage"
        E[Quadratic Speedup]
        F[Parallel Search]
        G[Amplitude Amplification]
        H[Optimal Performance]
    end

    A --> E
    B --> F
    C --> G
    D --> H

Custom Search Algorithms¶

class CustomQuantumSearch:
    def __init__(self, vectorstore):
        self.vectorstore = vectorstore
        self.quantum_backend = get_quantum_backend()

    async def adaptive_quantum_search(self, query, **kwargs):
        """Adaptive quantum search that selects optimal algorithm."""
        # Analyze query characteristics
        query_complexity = self.analyze_query_complexity(query)

        if query_complexity > 0.8:
            # Use amplitude amplification for complex queries
            return await self.amplitude_amplification_search(query)
        elif query_complexity > 0.5:
            # Use Grover's search for medium complexity
            return await self.grover_search(query)
        else:
            # Use classical search for simple queries
            return await self.classical_search(query)

📊 Performance Metrics¶

Search Performance Comparison¶

graph LR
    subgraph "Classical Performance"
        A[Linear - O(n)]
        B[Binary - O(log n)]
        C[Hash - O(1) avg]
        D[Limited Parallelism]
    end

    subgraph "Quantum Performance"
        E[Grover - O(sqrt n)]
        F[Quantum Walk - O(sqrt n)]
        G[Amplitude Amp - O(sqrt n)]
        H[Massive Parallelism]
    end

    subgraph "Hybrid Optimization"
        I[Adaptive Algorithm]
        J[Best of Both]
        K[Context Aware]
        L[Maximum Performance]
    end

    A --> I
    B --> J
    C --> K
    D --> L

    E --> I
    F --> J
    G --> K
    H --> L

🔒 License Integration¶

Retriever Restrictions¶

graph LR
    subgraph "License Tiers"
        A[Basic - Classical Only]
        B[Professional - Grover Search]
        C[Enterprise - All Algorithms]
        D[Research - Custom Algorithms]
    end

    subgraph "Performance Limits"
        E[1K Documents]
        F[10K Documents]
        G[100K+ Documents]
        H[Unlimited]
    end

    A --> E
    B --> F
    C --> G
    D --> H

License Enforcement¶

from quantumlangchain.licensing import requires_license

class QuantumRetriever(LicensedComponent):
    @requires_license(tier="basic")
    def __init__(self, **kwargs):
        """Initialize with license validation."""
        super().__init__(
            required_features=["quantum_retrieval"],
            required_tier="basic"
        )

    @requires_license(tier="professional")
    def enable_grover_search(self):
        """Enable Grover's search (Professional+ only)."""
        pass

    @requires_license(tier="enterprise")
    def enable_advanced_algorithms(self):
        """Enable advanced search algorithms (Enterprise+ only)."""
        pass

📚 API Reference¶

Core Retriever Classes¶

class QuantumRetriever:
    def __init__(self, vectorstore, search_algorithm="grover", **config):
        """Initialize quantum retriever."""

    async def aget_relevant_documents(self, query: str, **kwargs) -> List[Document]:
        """Get relevant documents asynchronously."""

    def get_relevant_documents(self, query: str, **kwargs) -> List[Document]:
        """Get relevant documents synchronously."""

    async def quantum_search(self, query: str, **kwargs) -> List[Document]:
        """Perform quantum-enhanced search."""

🔐 License Requirements¶

• Basic Retriever: Basic license tier (classical search only)
• Professional Retriever: Professional license tier (Grover search)
• Enterprise Retriever: Enterprise license tier (all algorithms)
• Research Retriever: Research license tier (custom algorithms)

Contact bajpaikrishna715@gmail.com for licensing.

Quantum Retriever provides unprecedented search capabilities for next-generation AI applications.