⚙️ Configuration Guide¶

🔧 Basic Configuration¶

Environment Setup¶

import quantumlangchain as qlc

# Set global configuration
qlc.configure({
    "default_backend": "qiskit",
    "cache_enabled": True,
    "debug_mode": False,
    "auto_fallback": True
})

Configuration File¶

Create a quantum_config.yaml:

# quantum_config.yaml
quantum:
  default_backend: "qiskit"
  simulation:
    shots: 1024
    noise_model: false
  memory:
    classical_dim: 512
    quantum_dim: 8
  licensing:
    check_interval: 3600  # seconds
    grace_period: 86400   # 24 hours

Load configuration:

import quantumlangchain as qlc

# Load from file
qlc.load_config("quantum_config.yaml")

# Or set programmatically
config = qlc.QuantumConfig()
config.set("quantum.default_backend", "qiskit")

🖥️ Backend Configuration¶

Qiskit Backend¶

from quantumlangchain.backends import QiskitBackend

# Local simulator
qiskit_config = {
    "simulator": "aer_simulator",
    "shots": 1024,
    "optimization_level": 1,
    "noise_model": None,
    "coupling_map": None
}

backend = QiskitBackend(**qiskit_config)

PennyLane Backend¶

from quantumlangchain.backends import PennyLaneBackend

# PennyLane configuration
pennylane_config = {
    "device": "default.qubit",
    "shots": 1000,
    "diff_method": "adjoint",
    "interface": "autograd"
}

backend = PennyLaneBackend(**pennylane_config)

Amazon Braket Backend¶

from quantumlangchain.backends import BraketBackend

# Braket configuration
braket_config = {
    "device_name": "braket_sv",
    "local_simulator": True,
    "s3_bucket": "your-braket-bucket",
    "s3_prefix": "quantum-results"
}

backend = BraketBackend(**braket_config)

Backend Selection¶

# Automatic backend selection
backend = qlc.auto_select_backend(
    preference_order=["qiskit", "pennylane", "braket"],
    requirements={
        "min_qubits": 8,
        "supports_noise": True,
        "local_simulation": True
    }
)

🧠 Memory Configuration¶

Basic Memory Setup¶

from quantumlangchain import QuantumMemory

memory_config = {
    "classical_dim": 512,           # Classical embedding dimension
    "quantum_dim": 8,              # Number of qubits
    "entanglement_layers": 3,      # Entanglement circuit depth
    "decoherence_rate": 0.01,      # Simulated decoherence
    "error_correction": True,       # Enable quantum error correction
    "compression_enabled": True,    # Memory compression
    "persistence": True,           # Save to disk
    "cache_size": 1000            # Cache size in MB
}

memory = QuantumMemory(**memory_config)

Advanced Memory Options¶

# Memory with custom initialization
memory = QuantumMemory(
    classical_dim=1024,
    quantum_dim=16,

    # Entanglement configuration
    entanglement_type="linear",  # "linear", "all_to_all", "ring"
    entanglement_strength=0.8,

    # Decoherence simulation
    decoherence_model="exponential",
    T1_time=50e-6,  # Relaxation time
    T2_time=20e-6,  # Dephasing time

    # Error correction
    error_correction_code="surface",  # "surface", "color", "repetition"
    error_threshold=0.01,

    # Performance optimization
    vectorization=True,
    parallel_operations=True,
    batch_size=32
)

🔗 Chain Configuration¶

Basic Chain Setup¶

from quantumlangchain import QLChain

chain_config = {
    "memory": memory,
    "backend": backend,
    "max_iterations": 10,
    "convergence_threshold": 0.95,
    "decoherence_threshold": 0.1,
    "quantum_enhancement": True,
    "fallback_classical": True,
    "verbose": False
}

chain = QLChain(**chain_config)

Advanced Chain Configuration¶

# Chain with sophisticated control
chain = QLChain(
    memory=memory,
    backend=backend,

    # Execution control
    max_iterations=20,
    convergence_threshold=0.98,
    early_stopping=True,
    timeout_seconds=300,

    # Quantum behavior
    decoherence_threshold=0.05,
    entanglement_threshold=0.3,
    quantum_enhancement_level="high",  # "low", "medium", "high"

    # Optimization
    circuit_optimization=True,
    gate_fusion=True,
    parallel_execution=True,

    # Fallback and error handling
    classical_fallback=True,
    error_mitigation=True,
    retry_failed_operations=3,

    # Monitoring
    performance_tracking=True,
    circuit_profiling=True,
    memory_usage_tracking=True
)

🤖 Agent Configuration¶

Basic Agent Setup¶

from quantumlangchain import EntangledAgents

agent_config = {
    "agent_count": 3,
    "shared_memory": shared_memory,
    "entanglement_topology": "all_to_all",
    "communication_protocol": "quantum",
    "consensus_threshold": 0.8,
    "max_collaboration_rounds": 5
}

agents = EntangledAgents(**agent_config)

Advanced Agent Configuration¶

# Sophisticated multi-agent system
agents = EntangledAgents(
    agent_count=5,
    shared_memory=shared_memory,

    # Agent specialization
    specializations=["theory", "implementation", "testing", "optimization", "validation"],
    skill_weights=[0.9, 0.8, 0.7, 0.8, 0.9],

    # Communication topology
    entanglement_topology="hierarchical",  # "all_to_all", "ring", "star", "hierarchical"
    communication_bandwidth=100,  # Operations per second

    # Collaboration dynamics
    consensus_algorithm="quantum_voting",
    voting_weights="expertise_based",
    conflict_resolution="interference_based",

    # Performance optimization
    parallel_agent_execution=True,
    load_balancing=True,
    dynamic_task_allocation=True,

    # Monitoring
    agent_performance_tracking=True,
    collaboration_analytics=True,
    emergent_behavior_detection=True
)

🔍 Retrieval Configuration¶

Basic Retriever Setup¶

from quantumlangchain import QuantumRetriever, HybridChromaDB

# Vector store configuration
vectorstore_config = {
    "collection_name": "quantum_knowledge",
    "classical_embeddings": True,
    "quantum_embeddings": True,
    "embedding_dimension": 512,
    "distance_metric": "cosine",
    "persistence_enabled": True
}

vectorstore = HybridChromaDB(**vectorstore_config)

# Retriever configuration
retriever_config = {
    "vectorstore": vectorstore,
    "top_k": 5,
    "score_threshold": 0.7,
    "quantum_enhanced": True,
    "grover_iterations": 2
}

retriever = QuantumRetriever(**retriever_config)

Advanced Retrieval Configuration¶

# Quantum-enhanced retrieval system
retriever = QuantumRetriever(
    vectorstore=vectorstore,

    # Basic retrieval
    top_k=10,
    score_threshold=0.6,
    max_results=50,

    # Quantum enhancement
    quantum_speedup=True,
    grover_iterations=3,
    amplitude_amplification=True,
    quantum_similarity_metric="fidelity",

    # Semantic processing
    semantic_clustering=True,
    concept_extraction=True,
    relevance_reranking=True,

    # Performance optimization
    caching_enabled=True,
    parallel_search=True,
    index_optimization=True,

    # Quality control
    result_filtering=True,
    duplicate_removal=True,
    coherence_checking=True
)

📊 Monitoring & Logging¶

Basic Monitoring¶

import quantumlangchain as qlc

# Enable monitoring
qlc.enable_monitoring({
    "performance_metrics": True,
    "quantum_state_tracking": True,
    "memory_usage": True,
    "error_tracking": True
})

# Set logging level
qlc.set_log_level("INFO")  # "DEBUG", "INFO", "WARNING", "ERROR"

Advanced Monitoring¶

# Comprehensive monitoring setup
monitoring_config = {
    "metrics": {
        "execution_time": True,
        "quantum_fidelity": True,
        "decoherence_levels": True,
        "entanglement_measures": True,
        "memory_coherence": True,
        "agent_collaboration": True
    },
    "logging": {
        "level": "DEBUG",
        "file_output": "quantum_logs.log",
        "structured_logging": True,
        "include_quantum_states": False  # Large data
    },
    "alerts": {
        "decoherence_threshold": 0.8,
        "memory_usage_threshold": 0.9,
        "error_rate_threshold": 0.1
    },
    "export": {
        "prometheus_metrics": True,
        "json_export": True,
        "interval_seconds": 60
    }
}

qlc.configure_monitoring(monitoring_config)

🔒 Security Configuration¶

License Security¶

# Secure license configuration
license_config = {
    "validation_endpoint": "https://api.quantumlangchain.com/validate",
    "certificate_path": "/path/to/cert.pem",
    "encryption_enabled": True,
    "tamper_detection": True,
    "offline_validation": True,
    "grace_period_hours": 24
}

qlc.configure_licensing(license_config)

Data Security¶

# Data protection configuration
security_config = {
    "encryption": {
        "at_rest": True,
        "in_transit": True,
        "quantum_states": True,
        "algorithm": "AES-256"
    },
    "access_control": {
        "role_based": True,
        "audit_logging": True,
        "session_timeout": 3600
    },
    "quantum_security": {
        "measurement_protection": True,
        "state_privacy": True,
        "entanglement_security": True
    }
}

qlc.configure_security(security_config)

🌐 Environment Variables¶

# .env file configuration
QUANTUMLANGCHAIN_LICENSE_PATH=/path/to/license.json
QUANTUMLANGCHAIN_BACKEND=qiskit
QUANTUMLANGCHAIN_DEBUG=false
QUANTUMLANGCHAIN_CACHE_DIR=/tmp/qlc_cache
QUANTUMLANGCHAIN_LOG_LEVEL=INFO

# Backend-specific
QISKIT_SHOTS=1024
PENNYLANE_DEVICE=default.qubit
BRAKET_S3_BUCKET=my-braket-bucket

# Security
QUANTUMLANGCHAIN_ENCRYPTION_KEY=your-secret-key
QUANTUMLANGCHAIN_VERIFY_SSL=true

📁 File Structure¶

project/
├── quantum_config.yaml          # Main configuration
├── .env                         # Environment variables
├── data/
│   ├── quantum_memory.db       # Persistent memory
│   ├── cache/                  # Cache files
│   └── logs/                   # Log files
├── licenses/
│   └── quantumlangchain.json   # License file
└── configs/
    ├── development.yaml        # Dev config
    ├── production.yaml         # Prod config
    └── testing.yaml           # Test config

🔄 Configuration Management¶

Dynamic Configuration¶

# Runtime configuration updates
config = qlc.get_config()

# Update backend settings
config.update("quantum.backend.shots", 2048)

# Update memory settings
config.update("quantum.memory.quantum_dim", 16)

# Apply changes
qlc.apply_config_updates()

Configuration Validation¶

# Validate configuration
validation_result = qlc.validate_config()

if not validation_result.is_valid:
    print("Configuration errors:")
    for error in validation_result.errors:
        print(f"  - {error}")
else:
    print("✅ Configuration is valid")

This comprehensive configuration guide covers all aspects of setting up and optimizing QuantumLangChain for your specific needs.