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QAM Tutorials

This guide provides detailed, step-by-step tutorials for common QAM use cases. Each tutorial includes comprehensive explanations and practical examples.

1. Quantum Cluster Orchestration

Understanding Cluster Orchestration

Cluster orchestration in QAM combines quantum optimization with hierarchical management to efficiently organize and coordinate large agent systems. The EnhancedQUBOScheduler provides advanced scheduling capabilities using quantum-inspired algorithms.

Implementation Steps

from qam.enhanced_scheduler import EnhancedQUBOScheduler
from qam.cluster_management import ClusterManager

# Initialize components
manager = ClusterManager()
scheduler = EnhancedQUBOScheduler()

# Create sample tasks with varying requirements
tasks = [
    {
        "id": f"task{i}",
        "requirements": {
            "cpu": 2,
            "memory": 4096,
            "priority": i % 3
        }
    } for i in range(100)
]

# Optimize cluster structure
clusters = manager.optimize_cluster_structure()

# Build hierarchical QUBO for task scheduling
qubo_levels = scheduler.build_hierarchical_qubo(
    tasks=tasks,
    clusters=clusters,
    max_cluster_size=100
)

# Optimize cluster assignments
assignments = scheduler.optimize_cluster_assignments(tasks, clusters)

print("Task Assignments:")
for task_id, cluster_id in assignments.items():
    print(f"Task {task_id} → Cluster {cluster_id}")

Monitoring and Analysis

# Monitor cluster performance
for cluster_id, cluster_info in clusters.items():
    metrics = manager.get_performance_metrics(cluster_id)
    print(f"\nCluster {cluster_id} Performance:")
    print(f"Success Rate: {metrics['success_rate']:.2f}")
    print(f"Resource Utilization: {metrics['utilization']:.2f}")

2. Azure Quantum Integration

Setting Up Azure Quantum

Before submitting jobs, ensure proper configuration of your Azure Quantum workspace and authentication.

from qam.azure_quantum import AzureQuantumClient, AzureQuantumConfig
import os

# Load configuration from environment
config = AzureQuantumConfig(
    resource_group=os.getenv("AZURE_RESOURCE_GROUP"),
    workspace_name=os.getenv("AZURE_QUANTUM_WORKSPACE"),
    location=os.getenv("AZURE_LOCATION"),
    subscription_id=os.getenv("AZURE_SUBSCRIPTION_ID")
)

# Initialize client
client = AzureQuantumClient(config)

# Verify connection
client._check_azure_cli()

Submitting and Managing Jobs

# Prepare QUBO problem
qubo_problem = {
    "problem_type": "pubo",
    "version": "1.0",
    "terms": [
        {"c": 1.0, "ids": [0]},
        {"c": -0.5, "ids": [0, 1]},
        {"c": 0.3, "ids": [1, 2]}
    ]
}

# Submit job
job_id = client.submit_qubo(qubo_problem)
print(f"Submitted job: {job_id}")

# Monitor progress
status = client.get_job_status(job_id)
print(f"Job status: {status}")

# Wait for completion and get results
try:
    result = client.wait_for_job(job_id, timeout_seconds=300)
    print("\nJob Results:")
    print(f"Solution: {result['solutions'][0]['configuration']}")
    print(f"Energy: {result['solutions'][0]['cost']}")
except TimeoutError:
    print("Job exceeded timeout period")

3. QAOA Optimization

Understanding QAOA

The Quantum Approximate Optimization Algorithm (QAOA) is a hybrid quantum-classical algorithm for solving combinatorial optimization problems. QAM implements QAOA for enhanced scheduling decisions.

Basic QAOA Implementation

from qam.qaoa_optimizer import QAOAOptimizer
import numpy as np

# Create problem Hamiltonian
problem_hamiltonian = np.array([
    [1, -1, 0, 0],
    [-1, 2, -1, 0],
    [0, -1, 2, -1],
    [0, 0, -1, 1]
])

# Initialize optimizer with custom parameters
optimizer = QAOAOptimizer()
optimizer.circuit_parameters.update({
    'p_steps': 3,
    'learning_rate': 0.05,
    'max_iterations': 200
})

# Run optimization
result = optimizer.optimize(problem_hamiltonian)

print("\nOptimization Results:")
print(f"Solution: {result.solution}")
print(f"Energy: {result.energy}")
print(f"Success: {result.success}")
print(f"Iterations: {result.iterations}")

Advanced QAOA Features

# Custom initial state
n_qubits = int(np.log2(problem_hamiltonian.shape[0]))
initial_state = np.ones(2**n_qubits) / np.sqrt(2**n_qubits)

# Optimize with custom state
result = optimizer.optimize(
    problem_hamiltonian,
    initial_state=initial_state
)

# Analyze optimization history
history = optimizer.get_optimization_history()
for i, opt_result in enumerate(history):
    print(f"\nOptimization {i+1}:")
    print(f"Parameters: {opt_result.parameters}")
    print(f"Energy Evolution: {opt_result.history}")

Advanced Topics →