🏗️ Architecture Overview¶

class QuantumLangChainCore:
    """
    Central engine coordinating quantum-classical operations
    Contact: bajpaikrishna715@gmail.com for enterprise licensing
    """

    def __init__(self):
        self.license_manager = LicenseManager()
        self.quantum_manager = QuantumManager()
        self.chain_manager = ChainManager()
        self.memory_manager = MemoryManager()

    @requires_license
    async def initialize(self):
        """Initialize all subsystems with license validation"""
        await self.license_manager.validate()
        await self.quantum_manager.initialize_backends()
        await self.chain_manager.setup_quantum_chains()
        await self.memory_manager.configure_quantum_memory()

class LicenseManager:
    """
    Comprehensive license management system

    Features:
    - Real-time license validation
    - Grace period management (24 hours)
    - Feature gating and compliance
    - Usage analytics and reporting
    """

    def __init__(self):
        self.license_cache = LicenseCache()
        self.validator = LicenseValidator()
        self.grace_period = GracePeriodManager()

    async def validate_license(self) -> LicenseStatus:
        """Validate current license status"""
        if self.grace_period.is_active():
            return LicenseStatus.GRACE_PERIOD

        license_data = await self.license_cache.get_license()
        return await self.validator.validate(license_data)

class QuantumBackend(ABC):
    """Abstract base class for quantum computing backends"""

    @abstractmethod
    async def execute_circuit(self, circuit: QuantumCircuit) -> QuantumResult:
        """Execute quantum circuit and return results"""
        pass

    @abstractmethod
    async def get_backend_info(self) -> BackendInfo:
        """Get backend capabilities and status"""
        pass

    @abstractmethod
    async def optimize_circuit(self, circuit: QuantumCircuit) -> QuantumCircuit:
        """Optimize circuit for this backend"""
        pass

class QiskitBackend(QuantumBackend):
    """IBM Qiskit backend implementation"""

    def __init__(self, provider: str = "aer", backend: str = "aer_simulator"):
        self.provider = provider
        self.backend_name = backend
        self.backend = None

    async def execute_circuit(self, circuit: QuantumCircuit) -> QuantumResult:
        """Execute circuit on Qiskit backend"""
        job = await self.backend.run(circuit, shots=self.shots)
        result = await job.result()
        return QuantumResult.from_qiskit(result)

class QuantumMemory:
    """
    Quantum-enhanced memory system with entanglement capabilities

    Architecture:
    - Classical vector storage for traditional embeddings
    - Quantum state storage for superposition-based memory
    - Entanglement networks for associative memory
    - Temporal coherence for timeline management
    """

    def __init__(self, classical_dim: int = 512, quantum_dim: int = 8):
        self.classical_memory = ClassicalVectorStore(dim=classical_dim)
        self.quantum_memory = QuantumStateMemory(qubits=quantum_dim)
        self.entanglement_network = EntanglementNetwork()
        self.temporal_coherence = TemporalCoherence()

    @requires_license
    async def store_memory(self, content: str, quantum_enhanced: bool = True):
        """Store memory with optional quantum enhancement"""
        classical_embedding = await self.embed_classical(content)
        await self.classical_memory.store(classical_embedding)

        if quantum_enhanced:
            quantum_state = await self.create_quantum_state(content)
            await self.quantum_memory.store(quantum_state)
            await self.entanglement_network.create_associations(quantum_state)

class QuantumChain:
    """
    Quantum-enhanced processing chain

    Features:
    - Adaptive quantum/classical switching
    - Superposition-based parallel processing
    - Entanglement for context preservation
    - Quantum error correction
    """

    def __init__(self, quantum_threshold: float = 0.7):
        self.quantum_threshold = quantum_threshold
        self.quantum_processor = QuantumProcessor()
        self.classical_processor = ClassicalProcessor()
        self.decision_engine = QuantumClassicalDecisionEngine()

    @requires_license
    async def process(self, input_data: Any) -> ChainResult:
        """Process input through quantum-enhanced chain"""
        complexity_score = await self.analyze_complexity(input_data)

        if complexity_score > self.quantum_threshold:
            return await self.quantum_processor.process(input_data)
        else:
            return await self.classical_processor.process(input_data)

class EntangledAgents:
    """
    Multi-agent system with quantum entanglement

    Capabilities:
    - Quantum state sharing between agents
    - Parallel problem solving in superposition
    - Consensus through quantum measurement
    - Distributed quantum computing
    """

    def __init__(self, agent_count: int = 3):
        self.agents = []
        self.entanglement_matrix = QuantumEntanglementMatrix(agent_count)
        self.consensus_engine = QuantumConsensusEngine()

    @requires_license
    async def collaborative_solve(self, problem: str) -> AgentResult:
        """Solve problem through quantum-entangled collaboration"""
        # Create quantum superposition of solution approaches
        quantum_state = await self.create_solution_superposition(problem)

        # Distribute entangled states to agents
        for i, agent in enumerate(self.agents):
            entangled_state = self.entanglement_matrix.get_state(i)
            await agent.receive_quantum_state(entangled_state)

        # Parallel processing in quantum superposition
        partial_solutions = await asyncio.gather(*[
            agent.process_quantum(quantum_state) for agent in self.agents
        ])

        # Quantum consensus measurement
        final_solution = await self.consensus_engine.measure_consensus(
            partial_solutions
        )

        return final_solution

class QuantumPerformanceMonitor:
    """
    Real-time monitoring of quantum system performance

    Metrics:
    - Quantum operation latency
    - Decoherence rates
    - Entanglement fidelity
    - Classical-quantum switching efficiency
    """

    def __init__(self):
        self.metrics_collector = QuantumMetricsCollector()
        self.performance_analyzer = PerformanceAnalyzer()
        self.optimization_engine = QuantumOptimizationEngine()

    async def monitor_execution(self, operation: QuantumOperation):
        """Monitor quantum operation performance"""
        start_time = time.time()

        # Collect pre-execution metrics
        pre_metrics = await self.metrics_collector.collect_state()

        # Execute operation
        result = await operation.execute()

        # Collect post-execution metrics
        post_metrics = await self.metrics_collector.collect_state()

        # Analyze performance
        performance_data = await self.performance_analyzer.analyze(
            pre_metrics, post_metrics, time.time() - start_time
        )

        # Suggest optimizations
        optimizations = await self.optimization_engine.suggest_improvements(
            performance_data
        )

        return PerformanceReport(result, performance_data, optimizations)

class QuantumSecurityManager:
    """
    Quantum-safe security management

    Features:
    - Quantum key distribution (QKD)
    - Post-quantum cryptography
    - Quantum random number generation
    - Secure multi-party quantum computation
    """

    def __init__(self):
        self.qkd_manager = QuantumKeyDistribution()
        self.post_quantum_crypto = PostQuantumCrypto()
        self.quantum_rng = QuantumRandomNumberGenerator()

    async def establish_secure_channel(self, parties: List[str]) -> SecureChannel:
        """Establish quantum-safe communication channel"""
        # Generate quantum keys
        quantum_keys = await self.qkd_manager.distribute_keys(parties)

        # Setup post-quantum encryption
        encryption_keys = await self.post_quantum_crypto.generate_keys()

        # Create secure channel
        channel = SecureChannel(quantum_keys, encryption_keys)

        return channel

class QuantumScalingManager:
    """
    Automatic scaling for quantum workloads

    Scaling Strategies:
    - Quantum resource pooling
    - Dynamic backend selection
    - Load balancing across quantum devices
    - Classical fallback for overflow
    """

    def __init__(self):
        self.resource_pool = QuantumResourcePool()
        self.load_balancer = QuantumLoadBalancer()
        self.auto_scaler = QuantumAutoScaler()

    async def scale_for_workload(self, workload: QuantumWorkload):
        """Automatically scale resources for workload"""
        # Analyze workload requirements
        requirements = await self.analyze_workload(workload)

        # Check available resources
        available_resources = await self.resource_pool.get_available()

        # Scale if needed
        if requirements.exceeds_capacity(available_resources):
            await self.auto_scaler.scale_up(requirements)

        # Distribute workload
        distribution = await self.load_balancer.distribute(
            workload, available_resources
        )

        return distribution

class IntegrationManager:
    """
    Manage integrations with external systems

    Supported Integrations:
    - LangChain compatibility layer
    - HuggingFace model integration
    - OpenAI API bridging
    - AWS Bedrock connectivity
    - Custom API endpoints
    """

    def __init__(self):
        self.adapters = {}
        self.register_adapters()

    def register_adapters(self):
        """Register all available integration adapters"""
        self.adapters['langchain'] = LangChainAdapter()
        self.adapters['huggingface'] = HuggingFaceAdapter()
        self.adapters['openai'] = OpenAIAdapter()
        self.adapters['bedrock'] = BedrockAdapter()