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QAM Quick Start Guide

Overview

The Quantum Agent Manager (QAM) is a powerful framework for quantum-inspired task scheduling and agent orchestration. It leverages quantum optimization techniques through Azure Quantum's services to solve complex scheduling problems efficiently.

Key Features

  • Quantum-inspired task scheduling
  • Multi-agent cluster management
  • Resource optimization using QUBO/QAOA
  • Quantum reasoning for decision making
  • Hierarchical optimization for large-scale problems

Installation

Prerequisites

  • Python 3.8+
  • Azure Quantum subscription
  • Azure CLI with quantum extension
# Clone the repository
git clone https://github.com/agenticsorg/Quantum-Agentic-Agents
cd Quantum-Agentic-Agents

# Install dependencies
pip install -r requirements.txt

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

# Verify Azure CLI installation
az --version

# Install Azure Quantum extension
az extension add -n quantum

# Login to Azure (follow the device code authentication process)
az login --use-device-code

Basic Configuration

Setting up Azure Quantum

The first step is configuring your Azure Quantum workspace. This provides access to quantum optimization solvers.

from qam.azure_quantum import AzureQuantumConfig, AzureQuantumClient

# Configure Azure Quantum workspace
config = AzureQuantumConfig(
    resource_group="quantum-resources",
    workspace_name="qam-production",
    location="eastus"
)

# Initialize client
client = AzureQuantumClient(config)

Environment Variables

Set up required environment variables for authentication:

export AZURE_SUBSCRIPTION_ID="your-subscription-id"
export AZURE_QUANTUM_WORKSPACE="your-workspace-name"

Your First Quantum-Optimized Schedule

1. Define a Simple Scheduling Problem

from qam.scheduler import QUBOScheduler

# Create a simple scheduling problem
qubo = {
    "problem": "ising",
    "terms": [
        {"c": 1, "ids": [0]},      # Weight for task 0
        {"c": -0.5, "ids": [0, 1]}  # Interaction between tasks 0 and 1
    ]
}

2. Submit and Monitor Job

# Submit to Azure Quantum
job_id = client.submit_qubo(qubo)

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

# Wait for results
result = client.wait_for_job(job_id)
print(f"Optimal schedule: {result['solutions'][0]['configuration']}")

Understanding the Results

The result contains:

  • configuration: Binary array representing task assignments
  • cost: Energy value of the solution (lower is better)
  • parameters: Solver parameters used

Next Steps

Common Issues and Solutions

Authentication Errors

If you encounter authentication issues:

  1. Verify Azure CLI installation
  2. Run az login --use-device-code and follow the device code authentication process
  3. Check environment variables
  4. Ensure quantum workspace exists

Job Submission Failures

Common causes:

  • Invalid QUBO format
  • Workspace quota exceeded
  • Network connectivity issues

Performance Optimization

Tips for better results:

  • Start with small problem sizes
  • Use appropriate solver types
  • Monitor quantum credits usage

Next: Basic Usage →