v1.2.0 - The Autonomous Pipeline

Release v1.2.0 - The Autonomous Pipeline

This is a major architectural release that elevates the project from a manually-executed pipeline to a truly autonomous, self-healing MLOps system. The core innovation is the implementation of a "closed-loop" mechanism that allows the system to monitor its own performance and automatically trigger corrective actions.

With this upgrade, the project now serves as a complete, functional blueprint for the next generation of resilient, operational AI, demonstrating how MLOps automation can be integrated even in a complex, hybrid quantum-classical framework.

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✨ New Feature: The Closed-Loop Retraining Trigger

The centerpiece of this release is the new self-healing capability, which functions like a thermostat for the AI model:

1. Detection (The Sensor): The monitor_with_qsvm.py script now analyzes the simulated production stream for data drift and calculates a drift_rate.
2. Signaling: If this drift rate exceeds a configurable threshold, the script writes a DRIFT_DETECTED signal to a status file.
3. Automated Trigger (The Wiring): The main CI/CD workflow (main.yml) reads this status file. Upon detecting the signal, it makes a secure API call to the GitHub Actions API.
4. Autonomous Action (The Furnace): This API call triggers a new, completely independent retraining workflow (retrain.yml), which re-executes the entire run_pipeline.py script. This automatically builds a new, updated model adapted to the new data realities.

This entire process is hands-off, creating a resilient system that can adapt to a changing environment without human intervention.

🔧 Foundational MLOps Features (from v1.1.x)

This new autonomous capability is built upon the solid, professional foundation established in previous versions, including:

• A Central "Control Panel" (config.yaml) for all parameters.
• Full MLflow Integration for complete experiment tracking and governance.
• A Modular, Simulator-First Architecture with optional noisy simulation for research.
• A Full Suite of Storytelling Visualizations to prove the value of each stage.
• A Robust CI/CD and Unit Testing Framework to ensure engineering quality.

📈 Future Work

This v1.2 release marks the completion of the core architectural vision. Future work will now focus on applying this powerful, autonomous framework to new research questions, such as:

• Benchmarking the pipeline's performance on real-world, complex datasets.
• Implementing and comparing different Quantum Error Mitigation (QEM) strategies.
• Exploring the trade-offs between retraining frequency and computational cost.

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v1.1.0 - MLflow Tracking

Release v1.1.0 - MLflow Tracking

This release marks a major architectural evolution of the project, transforming the initial "Blueprint" into a professional, configurable, and fully-tracked MLOps system. The core quantum algorithms remain the same, but the operational framework surrounding them is now significantly more robust, flexible, and reproducible.

With the integration of a central configuration file and the MLflow experiment tracking suite, the project now functions like a true "mission control" for running and analyzing quantum machine learning experiments.

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✨ Key Architectural Upgrades

• Configuration-Driven Pipeline: All hardcoded parameters (e.g., sample sizes, learning rates, epochs, visualization modes) have been decoupled from the source code and moved into a single, central config.yaml file. This enables rapid, code-free experimentation and enhances reproducibility.

• Full MLflow Integration: The entire pipeline is now wrapped in MLflow. Every execution of run_pipeline.py is logged as a distinct "run" and automatically captures:

  • Parameters: All settings from the config.yaml file are logged, providing a permanent record of the experimental setup.
  • Metrics: Key performance indicators from each stage (e.g., autoencoder loss, final classifier accuracy, anomalies detected) are tracked over time.
  • Artifacts: All outputs—including the trained feature extractor, the final tuned classifier, and all generated visualization plots—are saved and versioned within the MLflow run.

• Centralized MLOps Dashboard: Users can now launch a local web server with the mlflow ui command to access a powerful dashboard. This UI allows for comparing different runs, viewing metrics, and downloading any model or plot from any experiment.

📈 What's Next

With this robust MLOps foundation now in place, the project is perfectly positioned for the next phase of research and development:

• Connecting the pipeline to real quantum hardware backends.
• Implementing and testing Quantum Error Mitigation (QEM) techniques.
• "Closing the loop" with an automated retraining trigger in the CI/CD pipeline.

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To create this release, simply go to your repository's "Releases" page, click "Draft a new release," and enter the tag, title, and these notes. You now have a perfect snapshot of your professional MLOps system before you add the next layer of cutting-edge features.

v1.0.0 - The Blueprint

Release v1.0.0 - The Blueprint

This inaugural release marks the successful completion of a fully operational, end-to-end Quantum-Enhanced MLOps pipeline. This version serves as a stable, reproducible blueprint for integrating quantum algorithms into the modern machine learning lifecycle to address critical bottlenecks.

The entire pipeline, from data ingestion to a production-ready monitor, is fully automated and has been validated through a professional CI/CD workflow.

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✨ Key Features & Accomplishments

• [Stage 1] Quantum-Native Feature Extractor: A hybrid quantum-classical autoencoder (Qiskit + PyTorch) that has been successfully trained to generate powerful, low-dimensional feature representations from raw data.
• [Stage 2] Quantum-Accelerated HPO: A fully automated hyperparameter optimization process using the Quantum Approximate Optimization Algorithm (QAOA). The system successfully maps the classical search space to a QUBO problem and leverages a quantum solver to find optimal model parameters.
• [Stage 3] Quantum-Enhanced Production Monitor: A highly sensitive anomaly detector for production data drift, built by integrating a Qiskit FidelityQuantumKernel into a robust scikit-learn One-Class SVM.
• Visualisations: A full suite of plots for each stage that visually prove the value and function of each quantum component. Includes both fast-generating and high-quality options for flexible analysis.

🔧 MLOps & Engineering Excellence

• Automated CI/CD Pipeline: Integrated with GitHub Actions, the project now automatically performs linting, runs unit tests, and validates the entire end-to-end pipeline on every push to the main branch.
• Artifact Management: The CI/CD pipeline automatically archives the trained models and all generated visualizations as downloadable artifacts, ensuring reproducibility.
• Modular & Maintainable Code: The project has been refactored into a clean, modular structure, with a dedicated src directory, isolated visualization modules, and a professional .gitignore to preserve the repository structure without committing large data files.
• Unit Testing: A tests suite has been established to provide fast feedback on the integrity of core classical and quantum components during development.

📈 What's Next

This stable blueprint is now ready for future enhancements, including:

• Scaling up experiments with larger datasets and longer training times.
• Rigorous benchmarking against purely classical MLOps pipelines.
• Integration with a model registry like MLflow.
• Execution on real quantum hardware backends.