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QAM Basic Usage Guide

Core Concepts

Agent Clusters

Agent clusters are the fundamental organizational units in QAM. They enable efficient management of large-scale agent systems through hierarchical organization and quantum-optimized resource allocation.

from qam.cluster_management import ClusterManager, AgentCluster
from qam.quantum_reasoning import QuantumReACT

# Initialize cluster management
manager = ClusterManager()

# Create a new cluster
cluster = manager.create_cluster()

# Add agents to cluster
cluster.add_agent("agent1")
cluster.add_agent("agent2")

# Update resource requirements
cluster.update_resource_requirements({
    "cpu": 4,
    "memory": 8192,
    "qubits": 100
})

Resource Management

QAM provides sophisticated resource management capabilities, ensuring optimal distribution of computational and quantum resources across agent clusters.

from qam.resource_management import ResourceManager

# Initialize resource management
rm = ResourceManager()

# Add resource pools
rm.add_resource_pool("quantum_compute", 1000, "qubits")
rm.add_resource_pool("classical_compute", 5000, "cpu_cores")

# Allocate resources to clusters
allocation = rm.allocate_resource("quantum_compute", "cluster-1", 250)
print(f"Allocated {allocation.amount} qubits to {allocation.cluster_id}")

# Monitor resource utilization
metrics = rm._calculate_utilization()
print(f"Current utilization: {metrics}")

Quantum Reasoning Integration

The QuantumReACT engine enables quantum-inspired decision making, allowing agents to make optimal choices based on complex contextual information.

from qam.quantum_reasoning import QuantumReasoningState, QuantumReACT

# Initialize quantum reasoning
react_engine = QuantumReACT()
reasoning_state = QuantumReasoningState()

# Define decision context
context = {
    "available_actions": ["process_task", "delegate_task", "postpone_task"],
    "current_load": 0.75,
    "priority_level": "high",
    "resource_availability": 0.4
}

# Make quantum-informed decision
decision = react_engine.make_decision(context, reasoning_state)
print(f"Chosen action: {decision.action} (confidence: {decision.confidence})")

# Process outcome and update reasoning
outcome = Outcome(
    decision_id=decision.id,
    success=True,
    feedback={"completion_time": 120, "resource_usage": 0.3},
    timestamp=time.time()
)
react_engine.reflect_and_adjust(outcome, reasoning_state)

Performance Optimization

Resource Allocation Strategies

  1. Dynamic Scaling

    • Monitor resource utilization
    • Adjust allocations based on demand
    • Implement automatic scaling thresholds

  2. Quantum-Classical Hybrid Optimization

    • Use quantum resources for complex decisions
    • Leverage classical computing for routine tasks
    • Balance resource costs and performance

Cluster Management Best Practices

  1. Hierarchical Organization

    • Group related agents together
    • Maintain optimal cluster sizes (100-1000 agents)
    • Implement sub-clustering for large groups

  2. Resource Distribution

    • Allocate resources based on priority
    • Implement fair-sharing policies
    • Monitor and adjust based on utilization

Monitoring and Maintenance

Performance Metrics

# Get cluster performance metrics
cluster_metrics = cluster.get_performance_metrics()
print(f"Cluster efficiency: {cluster_metrics['efficiency']}")

# Monitor resource allocation
allocation_history = rm.get_allocation_history("quantum_compute")
for record in allocation_history:
    print(f"Time: {record.timestamp}, Amount: {record.amount}")

System Health Checks

# Check cluster health
for cluster_id, cluster in manager.root_clusters:
    metrics = cluster.get_total_resource_requirements()
    utilization = cluster.get_performance_metrics()
    print(f"Cluster {cluster_id}:")
    print(f"  Resources: {metrics}")
    print(f"  Utilization: {utilization}")

Error Handling and Recovery

Common Issues

  1. Resource Exhaustion

    • Implement graceful degradation
    • Use backup resource pools
    • Prioritize critical operations

  2. Decision Conflicts

    • Implement conflict resolution strategies
    • Use quantum reasoning for optimal choices
    • Maintain decision history for learning

Recovery Strategies

# Implement recovery from resource exhaustion
try:
    allocation = rm.allocate_resource("quantum_compute", "cluster-1", 1000)
except ResourceExhaustionError:
    # Fall back to classical computing
    allocation = rm.allocate_resource("classical_compute", "cluster-1", 100)
    
# Handle decision conflicts
if decision.confidence < 0.6:
    # Get alternative decision paths
    alternatives = react_engine.get_alternative_decisions(context, reasoning_state)
    # Choose best alternative
    decision = max(alternatives, key=lambda d: d.confidence)

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