Quantum Agent Manager (QAM)

Overview

QAM is a cutting-edge quantum-inspired task scheduling and agent orchestration system that leverages Azure Quantum's capabilities for optimizing multi-agent operations. By utilizing quantum computing principles, QAM can efficiently solve complex scheduling problems that would be computationally intensive for classical approaches.

Features

Core Capabilities

• Quantum-Optimized Scheduling: Utilizes Azure Quantum for solving QUBO-based scheduling problems
• Hierarchical Cluster Management: Organizes agents into optimized clusters for efficient task distribution
• Resource-Aware Optimization: Considers resource requirements and constraints in optimization decisions
• Quantum ReACT Integration: Quantum-enhanced reasoning for agent decision making
• Automatic Fallback Mechanisms: Graceful degradation to classical methods when quantum resources are unavailable

Advanced Features

• Parallel Quantum Job Processing: Submit multiple optimization jobs simultaneously
• Dynamic Resource Balancing: Automatically adjusts cluster sizes based on workload
• Quantum Circuit Execution: Direct quantum circuit implementation for specific optimization tasks
• Hybrid Optimization: Combines classical and quantum approaches for optimal performance

Benefits

• Superior Task Distribution: Quantum optimization finds better solutions than classical methods
• Scalable Architecture: Handles growing agent populations efficiently
• Resource Efficiency: Optimizes resource utilization across agent clusters
• Future-Proof Design: Ready for next-generation quantum hardware
• Robust Error Handling: Comprehensive fallback mechanisms ensure system reliability

Installation

Prerequisites

# Install Azure CLI
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash

# Verify installation
az --version

# Add quantum extension
az extension add -n quantum

# Login to Azure
az login --use-device-code

Package Installation

pip install -r requirements.txt

Azure Quantum Setup

from qam.azure_quantum import AzureQuantumConfig, AzureQuantumClient

# Configure workspace
config = AzureQuantumConfig(
    resource_group="your-resource-group",
    workspace_name="your-workspace",
    location="your-location",
    subscription_id="your-subscription-id"  # Optional
)

# Initialize client
client = AzureQuantumClient(config)

Basic Usage

Creating and Managing Clusters

from qam.cluster_management import ClusterManager

# Initialize manager
manager = ClusterManager()

# Create clusters
cluster1 = manager.create_cluster()
cluster2 = manager.create_cluster()

# Add agents
cluster1.add_agent("agent1")
cluster1.add_agent("agent2")

# Update resource requirements
cluster1.update_resource_requirements({
    "cpu": 2.0,
    "memory": 4.0
})

# Optimize cluster structure
assignments = manager.optimize_cluster_structure()

Quantum-Enhanced Scheduling

from qam.scheduler import QUBOScheduler
from qam.quantum_reasoning import QuantumReasoningState

# Initialize scheduler
scheduler = QUBOScheduler()

# Create quantum reasoning state
state = QuantumReasoningState()

# Define scheduling problem
horizon = 10
result = scheduler.optimize_schedule_with_reasoning(
    tasks=[...],  # Your tasks here
    horizon=horizon,
    reasoning_state=state
)

Advanced Usage

Custom Optimization Parameters

# Configure QUBO parameters
scheduler.base_weights = {
    "time_penalty": 1.0,
    "resource_penalty": 2.0,
    "dependency_penalty": 3.0
}

# Set quantum circuit parameters
scheduler.quantum_parameters = {
    "shots": 1000,
    "optimization_level": 2
}

Parallel Optimization

from qam.enhanced_scheduler import EnhancedQUBOScheduler

# Initialize enhanced scheduler
scheduler = EnhancedQUBOScheduler()

# Configure parallel jobs
scheduler.max_parallel_jobs = 4

# Run hierarchical optimization
levels = scheduler.build_hierarchical_qubo(
    tasks=[...],
    clusters={...},
    max_cluster_size=100
)

Custom Quantum Circuits

# Define custom quantum circuit
circuit = {
    "input_qubits": 2,
    "operations": [
        {"gate": "h", "targets": [0]},
        {"gate": "cnot", "controls": [0], "targets": [1]}
    ]
}

# Submit custom circuit
job_id = client.submit_qubo(circuit)
result = client.wait_for_job(job_id)

Configuration

Environment Variables

# Azure Quantum Configuration
export AZURE_SUBSCRIPTION_ID="your-subscription-id"
export AZURE_QUANTUM_WORKSPACE="your-workspace-name"
export AZURE_QUANTUM_LOCATION="your-location"

Optimization Parameters

# Cluster optimization
manager.optimization_parameters = {
    "target_cluster_size": 50,
    "split_threshold": 100,
    "merge_threshold": 20
}

# QUBO parameters
scheduler.optimization_parameters = {
    "timeout": 100,
    "beta_start": 0.1,
    "beta_stop": 1.0,
    "sweeps": 1000
}

Performance Tuning

Resource Optimization

• Set appropriate cluster size thresholds based on workload
• Adjust penalty weights for different optimization criteria
• Configure parallel job limits based on available quantum resources

Quantum Circuit Optimization

• Use appropriate number of qubits for problem size
• Adjust circuit depth based on quantum hardware capabilities
• Balance between quantum and classical processing

Error Handling

Quantum Resource Unavailability

try:
    result = scheduler.optimize_schedule_with_reasoning(...)
except Exception as e:
    # Fallback to classical optimization
    result = scheduler._classical_optimization()

Circuit Execution Errors

try:
    job_id = client.submit_qubo(problem)
    result = client.wait_for_job(job_id)
except Exception as e:
    print(f"Quantum job failed: {e}")
    # Implement fallback strategy

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

MIT License - see LICENSE file for details