🤝 Entangled Agents Module¶

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

Entangled Agents Architecture¶

graph TB
    subgraph "Agent Network"
        A[Agent Alpha]
        B[Agent Beta]
        C[Agent Gamma]
        D[Agent Delta]
    end

    subgraph "Entanglement Layer"
        E[Quantum Channels]
        F[Shared States]
        G[Non-local Correlations]
        H[Coherent Communication]
    end

    subgraph "Coordination Mechanisms"
        I[State Synchronization]
        J[Distributed Decision Making]
        K[Collective Intelligence]
        L[Emergent Behaviors]
    end

    subgraph "Control Systems"
        M[Entanglement Management]
        N[Decoherence Mitigation]
        O[Error Correction]
        P[Performance Monitoring]
    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 Capabilities¶

Quantum Communication¶

graph LR
    subgraph "Classical Communication"
        A[Message Passing]
        B[Sequential Processing]
        C[Limited Bandwidth]
        D[Information Loss]
    end

    subgraph "Quantum Communication"
        E[State Teleportation]
        F[Parallel Processing]
        G[Unlimited Bandwidth]
        H[Perfect Fidelity]
    end

    subgraph "Hybrid Protocol"
        I[Best of Both]
        J[Adaptive Selection]
        K[Context-Aware]
        L[Optimal Performance]
    end

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

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

Collective Intelligence¶

graph TB
    subgraph "Individual Agents"
        A[Local Knowledge]
        B[Local Processing]
        C[Local Decisions]
        D[Limited Perspective]
    end

    subgraph "Entangled Network"
        E[Shared Knowledge]
        F[Distributed Processing]
        G[Collective Decisions]
        H[Global Perspective]
    end

    subgraph "Emergent Properties"
        I[Swarm Intelligence]
        J[Distributed Cognition]
        K[Collective Problem Solving]
        L[Adaptive Coordination]
    end

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

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

🔧 Implementation¶

Basic Entangled Agent Network¶

from quantumlangchain.agents import EntangledAgent, AgentNetwork

# Create individual agents
agent_a = EntangledAgent(
    name="alpha",
    role="coordinator",
    quantum_dim=8,
    specialization="task_planning"
)

agent_b = EntangledAgent(
    name="beta",
    role="executor",
    quantum_dim=8,
    specialization="data_processing"
)

agent_c = EntangledAgent(
    name="gamma",
    role="analyzer",
    quantum_dim=8,
    specialization="pattern_recognition"
)

# Create entangled network
network = AgentNetwork(
    agents=[agent_a, agent_b, agent_c],
    entanglement_strength=0.8,
    topology="fully_connected"
)

# Establish quantum entanglement
await network.entangle_agents()

# Collaborative task execution
task = "Analyze large dataset and generate insights"
result = await network.collaborative_solve(task)

Advanced Multi-Agent System¶

from quantumlangchain.agents import (
    EntangledAgent,
    AgentNetwork,
    QuantumOrchestrator
)

class AdvancedAgentSystem:
    def __init__(self):
        # Specialized agent roles
        self.coordinator = EntangledAgent(
            role="coordinator",
            capabilities=["planning", "resource_allocation"],
            quantum_dim=12
        )

        self.researchers = [
            EntangledAgent(
                role="researcher",
                specialization=field,
                quantum_dim=8
            ) for field in ["physics", "chemistry", "biology", "cs"]
        ]

        self.analyzers = [
            EntangledAgent(
                role="analyzer",
                specialization=analysis_type,
                quantum_dim=8
            ) for analysis_type in ["statistical", "pattern", "semantic"]
        ]

        self.synthesizer = EntangledAgent(
            role="synthesizer",
            capabilities=["integration", "conclusion"],
            quantum_dim=12
        )

        # Create hierarchical network
        self.network = AgentNetwork(
            agents=([self.coordinator] + 
                   self.researchers + 
                   self.analyzers + 
                   [self.synthesizer]),
            topology="hierarchical",
            entanglement_strength=0.9
        )

        # Quantum orchestrator for coordination
        self.orchestrator = QuantumOrchestrator(
            network=self.network,
            coordination_strategy="quantum_consensus"
        )

    async def solve_complex_problem(self, problem):
        """Solve complex problem using entangled agents."""
        # Quantum planning phase
        plan = await self.coordinator.create_quantum_plan(problem)

        # Distributed research phase
        research_tasks = plan.decompose_into_research_tasks()
        research_results = await self.parallel_research(research_tasks)

        # Quantum analysis phase
        analysis_results = await self.quantum_analysis(research_results)

        # Synthesis and conclusion
        final_result = await self.synthesizer.synthesize_results(
            research_results, analysis_results
        )

        return final_result

    async def parallel_research(self, tasks):
        """Execute research tasks in parallel using entangled agents."""
        # Assign tasks to specialized researchers
        assignments = await self.orchestrator.assign_tasks(
            tasks, self.researchers
        )

        # Execute tasks with quantum acceleration
        results = []
        for agent, task in assignments:
            result = await agent.execute_with_entanglement(task)
            results.append(result)

        # Quantum consensus on results
        consensus_results = await self.orchestrator.quantum_consensus(
            results
        )

        return consensus_results

Quantum Swarm Intelligence¶

from quantumlangchain.agents import QuantumSwarm

class QuantumSwarmSystem:
    def __init__(self, swarm_size=50):
        # Create quantum swarm
        self.swarm = QuantumSwarm(
            size=swarm_size,
            quantum_dim=4,  # Each agent has 4 qubits
            entanglement_pattern="small_world",
            communication_protocol="quantum_gossip"
        )

        # Initialize swarm intelligence parameters
        self.pheromone_strength = 0.8
        self.exploration_rate = 0.3
        self.convergence_threshold = 0.95

    async def solve_optimization_problem(self, problem):
        """Solve optimization using quantum swarm intelligence."""
        # Initialize swarm with problem
        await self.swarm.initialize_problem(problem)

        # Quantum swarm optimization loop
        for iteration in range(self.max_iterations):
            # Each agent explores solution space
            await self.swarm.parallel_exploration()

            # Quantum communication between agents
            await self.swarm.quantum_information_sharing()

            # Update global best solution
            await self.swarm.update_global_optimum()

            # Check convergence
            if await self.swarm.check_convergence():
                break

        return await self.swarm.get_best_solution()

    async def adaptive_problem_solving(self, dynamic_problem):
        """Solve dynamic problems with adaptive swarm."""
        # Monitor problem changes
        while not dynamic_problem.is_solved():
            # Detect environment changes
            changes = await dynamic_problem.detect_changes()

            if changes:
                # Adapt swarm configuration
                await self.swarm.adapt_to_changes(changes)

            # Continue optimization
            await self.quantum_optimization_step()

            # Yield control for other tasks
            await asyncio.sleep(0.01)

🎯 Agent Types and Roles¶

Specialized Agent Architectures¶

graph TB
    subgraph "Coordinator Agents"
        A[Task Planner]
        B[Resource Manager]
        C[Quality Controller]
        D[System Monitor]
    end

    subgraph "Worker Agents"
        E[Data Processors]
        F[Pattern Analyzers]
        G[Content Generators]
        H[Decision Makers]
    end

    subgraph "Specialist Agents"
        I[Domain Experts]
        J[Method Specialists]
        K[Tool Operators]
        L[Interface Agents]
    end

    subgraph "Meta Agents"
        M[Learning Agents]
        N[Adaptation Agents]
        O[Evolution Agents]
        P[Emergence Monitors]
    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

Agent Communication Protocols¶

graph LR
    subgraph "Quantum Protocols"
        A[Quantum Teleportation]
        B[Superdense Coding]
        C[Quantum Key Distribution]
        D[Entanglement Swapping]
    end

    subgraph "Hybrid Protocols"
        E[Quantum-Classical Hybrid]
        F[Adaptive Protocol Selection]
        G[Context-Aware Communication]
        H[Error-Resilient Protocols]
    end

    subgraph "Network Topologies"
        I[Fully Connected]
        J[Hierarchical]
        K[Small World]
        L[Scale-Free]
    end

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    C --> G
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📊 Performance Characteristics¶

Scaling Properties¶

graph TB
    subgraph "Classical Multi-Agent"
        A[Linear Communication]
        B[Exponential Coordination Cost]
        C[Bottleneck Formation]
        D[Coordination Overhead]
    end

    subgraph "Quantum Multi-Agent"
        E[Parallel Communication]
        F[Polynomial Coordination]
        G[No Bottlenecks]
        H[Quantum Speedup]
    end

    subgraph "Performance Metrics"
        I[Task Completion Time]
        J[Communication Efficiency]
        K[Coordination Quality]
        L[Scalability Factor]
    end

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

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Entanglement Quality Metrics¶

graph LR
    subgraph "Entanglement Measures"
        A[Concurrence]
        B[Entanglement Entropy]
        C[Fidelity]
        D[Bell State Correlations]
    end

    subgraph "Network Metrics"
        E[Connectivity]
        F[Robustness]
        G[Coherence Time]
        H[Information Flow]
    end

    subgraph "Performance Indicators"
        I[Coordination Efficiency]
        J[Decision Quality]
        K[Adaptation Speed]
        L[Emergence Level]
    end

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

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

🛠️ Configuration and Management¶

Network Configuration¶

# Agent network configuration templates
NETWORK_CONFIGS = {
    "research_team": {
        "size": 5,
        "topology": "hierarchical",
        "roles": ["coordinator", "researcher", "analyzer", "critic", "synthesizer"],
        "entanglement_strength": 0.8,
        "specializations": {
            "researcher": ["literature_review", "data_collection"],
            "analyzer": ["statistical_analysis", "pattern_recognition"],
            "critic": ["quality_assessment", "peer_review"]
        }
    },

    "optimization_swarm": {
        "size": 100,
        "topology": "small_world",
        "roles": ["explorer", "exploiter", "messenger"],
        "entanglement_strength": 0.6,
        "parameters": {
            "exploration_rate": 0.3,
            "exploitation_rate": 0.7,
            "communication_frequency": 0.1
        }
    },

    "creative_collective": {
        "size": 8,
        "topology": "fully_connected",
        "roles": ["ideator", "evaluator", "refiner", "integrator"],
        "entanglement_strength": 0.9,
        "creativity_parameters": {
            "divergence_factor": 0.8,
            "convergence_factor": 0.6,
            "novelty_threshold": 0.7
        }
    }
}

Entanglement Management¶

graph TB
    subgraph "Entanglement Creation"
        A[Agent Pairing]
        B[State Preparation]
        C[Entangling Operations]
        D[Verification]
    end

    subgraph "Entanglement Maintenance"
        E[Decoherence Monitoring]
        F[Error Detection]
        G[Correction Protocols]
        H[State Refreshing]
    end

    subgraph "Entanglement Optimization"
        I[Strength Tuning]
        J[Topology Adaptation]
        K[Dynamic Reconfiguration]
        L[Performance Optimization]
    end

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

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

🔒 License Integration¶

Agent Network Restrictions¶

graph LR
    subgraph "License Tiers"
        A[Basic - 3 Agents]
        B[Professional - 10 Agents]
        C[Enterprise - 50 Agents]
        D[Research - Unlimited]
    end

    subgraph "Entanglement Features"
        E[Pair Entanglement]
        F[Multi-party Entanglement]
        G[Network Entanglement]
        H[Custom Protocols]
    end

    subgraph "Advanced Capabilities"
        I[Basic Coordination]
        J[Quantum Consensus]
        K[Swarm Intelligence]
        L[Emergent Behaviors]
    end

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

    E --> I
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License Enforcement¶

from quantumlangchain.licensing import requires_license

class EntangledAgent(LicensedComponent):
    @requires_license(tier="basic")
    def __init__(self, name, role, **kwargs):
        """Initialize entangled agent with license validation."""
        super().__init__(
            required_features=["entangled_agents"],
            required_tier="basic"
        )

        self.name = name
        self.role = role
        self._validate_configuration(kwargs)

    def _validate_configuration(self, config):
        """Validate configuration against license limits."""
        max_agents = self._get_max_agents()

        if len(AgentNetwork.get_all_agents()) >= max_agents:
            raise LicenseError(
                f"Agent limit exceeded. "
                f"License allows {max_agents} agents maximum. "
                f"Contact: bajpaikrishna715@gmail.com "
                f"Machine ID: {self.get_machine_id()}"
            )

class AgentNetwork(LicensedComponent):
    @requires_license(tier="professional")
    def enable_quantum_consensus(self):
        """Enable quantum consensus (Professional+ only)."""
        pass

    @requires_license(tier="enterprise")
    def enable_swarm_intelligence(self):
        """Enable swarm intelligence (Enterprise+ only)."""
        pass

    @requires_license(tier="research")
    def enable_emergent_behaviors(self):
        """Enable emergent behavior research (Research only)."""
        pass

🎯 Use Cases and Applications¶

Scientific Research¶

graph TB
    subgraph "Research Team"
        A[Principal Investigator]
        B[Data Scientist]
        C[Domain Expert]
        D[Statistician]
    end

    subgraph "Research Process"
        E[Hypothesis Generation]
        F[Experiment Design]
        G[Data Collection]
        H[Analysis & Interpretation]
    end

    subgraph "Quantum Enhancement"
        I[Parallel Hypothesis Testing]
        J[Quantum-Enhanced Analysis]
        K[Entangled Peer Review]
        L[Collective Intelligence]
    end

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

    E --> I
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Business Applications¶

# Business intelligence multi-agent system
class BusinessIntelligenceSystem:
    def __init__(self):
        # Market analysis agents
        self.market_analysts = [
            EntangledAgent(
                role="market_analyst",
                specialization=market,
                quantum_dim=6
            ) for market in ["stocks", "bonds", "commodities", "crypto"]
        ]

        # Risk assessment agents
        self.risk_assessors = [
            EntangledAgent(
                role="risk_assessor",
                specialization=risk_type,
                quantum_dim=6
            ) for risk_type in ["credit", "market", "operational", "liquidity"]
        ]

        # Strategy agents
        self.strategists = [
            EntangledAgent(
                role="strategist",
                specialization="portfolio_optimization",
                quantum_dim=8
            )
        ]

        # Create entangled network
        self.network = AgentNetwork(
            agents=(self.market_analysts + 
                   self.risk_assessors + 
                   self.strategists),
            topology="hierarchical",
            entanglement_strength=0.85
        )

    async def generate_investment_strategy(self, portfolio, constraints):
        """Generate optimal investment strategy."""
        # Parallel market analysis
        market_analysis = await self.parallel_market_analysis()

        # Quantum risk assessment
        risk_profile = await self.quantum_risk_assessment(portfolio)

        # Entangled strategy optimization
        strategy = await self.optimize_strategy(
            market_analysis, risk_profile, constraints
        )

        return strategy

Creative Applications¶

graph LR
    subgraph "Creative Agents"
        A[Idea Generator]
        B[Style Specialist]
        C[Content Creator]
        D[Quality Evaluator]
    end

    subgraph "Creative Process"
        E[Brainstorming]
        F[Concept Development]
        G[Content Creation]
        H[Refinement]
    end

    subgraph "Quantum Creativity"
        I[Superposition Ideas]
        J[Entangled Styles]
        K[Quantum Inspiration]
        L[Collective Creativity]
    end

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

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

📚 API Reference¶

Core Agent Classes¶

class EntangledAgent:
    def __init__(self, name: str, role: str, **config):
        """Initialize entangled agent."""

    async def entangle_with(self, other_agent: 'EntangledAgent') -> bool:
        """Establish quantum entanglement with another agent."""

    async def communicate(self, message: Any, target: str = None) -> Any:
        """Quantum communication with entangled agents."""

    async def collaborate(self, task: Task, partners: List['EntangledAgent']) -> Result:
        """Collaborate with other agents on a task."""

    def get_entanglement_state(self) -> QuantumState:
        """Get current entanglement state."""

class AgentNetwork:
    def __init__(self, agents: List[EntangledAgent], **config):
        """Initialize agent network."""

    async def entangle_all(self) -> bool:
        """Establish entanglement across the network."""

    async def broadcast(self, message: Any, sender: str) -> None:
        """Broadcast message to all agents."""

    async def consensus(self, question: str) -> ConsensusResult:
        """Reach quantum consensus on a question."""

    def get_network_state(self) -> NetworkState:
        """Get current network state."""

Specialized Network Types¶

class QuantumSwarm(AgentNetwork):
    async def swarm_optimization(self, objective: Callable) -> Solution:
        """Perform swarm optimization."""

    async def adaptive_behavior(self, environment: Environment) -> Behavior:
        """Adapt behavior to environment changes."""

class HierarchicalNetwork(AgentNetwork):
    async def hierarchical_processing(self, task: Task) -> Result:
        """Process task through hierarchy."""

    def get_hierarchy_structure(self) -> HierarchyTree:
        """Get current hierarchy structure."""

class CreativeCollective(AgentNetwork):
    async def creative_synthesis(self, prompt: str) -> CreativeOutput:
        """Generate creative output collaboratively."""

    async def style_transfer(self, content: Any, style: Style) -> Any:
        """Transfer style using collective intelligence."""

🔮 Future Developments¶

Planned Enhancements¶

graph TB
    subgraph "Near Term"
        A[Better Entanglement Protocols]
        B[Improved Coordination]
        C[Enhanced Communication]
        D[Optimized Performance]
    end

    subgraph "Medium Term"
        E[Fault-Tolerant Networks]
        F[Self-Organizing Systems]
        G[Adaptive Topologies]
        H[Emergent Intelligence]
    end

    subgraph "Long Term"
        I[Quantum Internet Integration]
        J[Conscious Agent Networks]
        K[Universal Collaboration]
        L[Transcendent Intelligence]
    end

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

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

🔐 License Requirements¶

Basic Agents: Basic license tier (up to 3 agents)
Professional Agents: Professional license tier (up to 10 agents)
Enterprise Agents: Enterprise license tier (up to 50 agents)
Research Agents: Research license tier (unlimited agents)

Contact bajpaikrishna715@gmail.com for licensing.

Entangled Agents represent the pinnacle of multi-agent AI systems, leveraging quantum entanglement for unprecedented coordination and collective intelligence capabilities.