Introducing Quantum Agentics: A New Way to Think About AI Tasks & Decision-Making

What if you could instantly see all the best solution to a complex reasoning problems all at once? That's the problem I'm trying to solve with Quantum Agentics. Traditional AI approaches like reinforcement learning struggle with interconnected decision-making because they evaluate actions sequentially, step by step. But quantum computing can consider many possibilities simultaneously, making it an ideal tool for agent-based task allocation.

Using Azure Quantum, this system leverages pure mathematical optimization and quantum principles to find the best way to distribute tasks among autonomous agents. Most people don't fully understand how quantum computing works, but in simple terms, it can represent and evaluate many possible task assignment at the same time, using superposition and interference to amplify the best solutions and discard bad ones. This makes it fundamentally different from other scheduling or learning-based approaches.

What makes this novel is that instead of relying on trial-and-error learning, it directly optimizes interconnected complexities, relationships between agents, and reasoning structures—similar to ReAct (Reasoning and Acting) in how it processes dependencies to find the optimal path.

Imagine your training system is like a super-smart assistant that can check millions of possible configurations at once. By using quantum annealing, which quickly explores many potential solutions simultaneously, and mixing it with traditional computing methods that are reliable and well-understood, the system quickly finds the best settings for your model.

The Quantum Training System applies these powerful quantum techniques to enhance model training and fine-tuning. By integrating quantum annealing and hybrid quantum-classical methods, the system rapidly converges on optimal model parameters and hyperparameters.

This means the training process becomes much faster and more accurate, using fewer resources and cutting costs—all managed by intelligent automated agents.

? Visit the Quantum Agentics Repo: https://github.com/agenticsorg/quantum-agentics

Overview

Quantum Agent Manager is a quantum-inspired task scheduling system designed for multi-agent environments. It leverages the Azure Quantum CLI to solve task allocation problems formulated as Quadratic Unconstrained Binary Optimization (QUBO) models. By automating the process of assigning tasks to agents, this system maximizes efficiency, balances workload, and minimizes overall completion time.

QAM Agents Features

Usage Cases and Industry Applications for QAM Agents

System Capabilities

Quantum Agent Manager (QAM)

Imagine instantly finding the best solution to a problem that would typically require hours of trial and error—this is what QAM Agents deliver. They smartly combine the fast, parallel processing capabilities of quantum annealing and hybrid quantum-classical methods with reliable, conventional algorithms. This blend not only accelerates the convergence to optimal solutions but also greatly enhances efficiency and scalability.

Agentics

QAM Agents form the core of an innovative quantum-powered system designed to supercharge decision-making and task execution. By harnessing quantum optimization techniques alongside traditional computing, these agents rapidly analyze complex scenarios to assign tasks, allocate resources, and fine-tune processes with exceptional speed and accuracy.

Whether you're a developer looking for a simple demonstration with the Hello World Agent, need robust task scheduling through the QAM Agent, or require specialized model training and fine-tuning via the Quantum Training Agent, our QAM Agents are engineered to meet those needs. With user-friendly interfaces, powerful APIs, and advanced error recovery features, the QAM Agents redefine intelligent automation and open new horizons in AI performance—all while being remarkably easy to integrate and use.

Agent Types

Quantum Training & Fine-Tuning System

Introducing our advanced agent-based Quantum Training & Fine-Tuning System—a revolutionary platform where cutting-edge agentics drive unparalleled efficiency in model training. This system harnesses the synergies of classical deep learning and quantum optimization techniques, enabling our intelligent agent to autonomously orchestrate training tasks.

By seamlessly integrating proven GPU-based methods with state-of-the-art quantum solvers, the agent precisely selects key parameters, fine-tunes hyperparameters, and optimizes training schedules, achieving results that traditional methods simply can’t match.

By formulating critical optimization problems as QUBO or QAOA tasks, the system leverages quantum annealing and hybrid quantum-classical solutions. Quantum annealing is a method that uses quantum physics to quickly find low-energy solutions for complex optimization problems, much like finding the lowest point in a hilly landscape. Hybrid quantum-classical solutions combine this quantum power with traditional computer techniques, allowing the system to efficiently solve parts of a problem using quantum methods while handling the rest with classical computing.

The system quickly finds the best model settings by rapidly converging to optimal solutions, which improves both the quality of the final model and how resources are used. The agentic approach automatically manages errors and adjusts resource allocation, ensuring that training runs smoothly and more efficiently, thus reducing both training time and costs.

Engineered for versatility, the system is designed with both developers and researchers in mind.

Beginners benefit from a user-friendly interface and extensive tutorials, while experts appreciate the detailed APIs and PhD-level analytical tools that empower in-depth performance analysis and scalability assessments. Experience the transformative power of our Quantum Training System, where intelligent agents redefine the limits of model training with innovative agentics for enhanced speed, quality, and scalability.

Usage

Performance Metrics

Implementation Status

Documentation

Getting Started

• Quick Start Guide
• Basic Usage
• Tutorials

Advanced Topics

• Advanced Topics
• PhD Level Research
• QAM Agent Guide

Implementation Details

• QAM Core Documentation
• Agents Overview
• Implementation Plans

API Reference

• OpenAPI Specification
• AsyncAPI Specification
• Agentics Manifest

License

MIT License - see LICENSE file for details

Installation

Prerequisites

• Python 3.8+
• Azure Quantum subscription
• Azure CLI with quantum extension

# Clone the repository
git clone https://github.com/agenticsorg/quantum-agentics
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

• Explore Basic Usage for core functionality
• Try the Tutorials for practical examples
• Learn about Advanced Features