qml-hcs 0.2.0

Quantum Machine Learning with hypercausal feedback for non-stationary environments.

Quantum Machine Learning Hypercausal System
A research-grade library for quantum-inspired machine learning with hypercausal feedback.

Quantum Machine Learning Hypercausal System

QML-HCS is a research-grade framework for constructing, simulating, and analyzing quantum-inspired machine learning architectures with hypercausal feedback mechanisms.
It integrates deterministic computation with causal inference and quantum-like superposition principles to explore emerging paradigms in Quantum Machine Learning (QML) and Causal Systems Theory.

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Overview

QML-HCS provides a modular and extensible environment for the study of hypercausal quantum models—systems that unify classical causal inference with quantum-inspired dynamics such as superposition, reversible transformations, and probabilistic branching.
It supports research into information propagation, causal stability, and consistency across interconnected quantum-like networks.

The framework is intended for scientific and engineering research in the following domains:

• Quantum Machine Learning: Development of quantum-inspired learning architectures.
• Causal Dynamics and Feedback Modeling: Formalization of recursive multi-branch causal systems.
• Hybrid Quantum–Classical Computation: Simulation of efficient causal propagation in hybrid systems.
• Counterfactual Simulation: Modeling systems capable of evaluating alternative causal scenarios.
• Algorithmic Benchmarking: Studying quantum-efficient learning and reasoning processes on classical hardware.

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Core Objectives

1. Hypercausal Feedback Modeling: Implement layered feedback systems capable of multi-directional causal propagation.
2. Quantum-Inspired Efficiency: Apply principles of superposition and entanglement to reduce computational cost.
3. Deterministic–Stochastic Integration: Provide configurable backends for deterministic, probabilistic, and mixed causal engines.
4. Scientific Transparency: Ensure reproducibility and open experimentation through standardized interfaces.
5. Scalability and Extensibility: Support modular expansion for backends, loss functions, and causal evaluators.

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Installation

Install the package directly from PyPI:

pip install qml-hcs

Install a specific version:

pip install qml-hcs==0.1.0

Verify installation:

python -c "import qmlhc; print(qmlhc.__version__)"

This mode is recommended for research and production environments where the source code remains static but full access to all APIs and modules is required.

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Getting Started

To verify installation, execute the minimal example:

qmlhc-demo

or run directly as a module:

python -m qmlhc.examples.ex_minimal_core_demo

Expected output (abridged):

=== Minimal Core Demo ===
output_dim (D):     3
branches (K):       3
...
HCModel.forward() matches single-node result ✔

Refer to the Getting Started Guide for further instructions.

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Examples

The repository provides several scientifically oriented demonstrations:

• Minimal hypercausal core operation
• Depth-dependent evaluation of feedback models
• Quantum-inspired benchmarking and stability testing
• Training with callback telemetry and adaptive losses
• Coherence and consistency experiments under stochastic variation

All examples are documented in the Examples Section.

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Intended Research Applications

QML-HCS serves as a research platform for the theoretical and experimental study of quantum-inspired machine learning.
It facilitates investigations in:

• Quantum-efficient learning architectures
• Simulation of adaptive feedback systems
• Analysis of causal consistency and information stability
• Hybrid quantum–classical training methodologies
• Exploration of hypercausal structures for predictive and inferential modeling

By providing a deterministic yet quantum-compatible environment, QML-HCS enables the testing of emerging theories in quantum-causal computation without the need for specialized quantum hardware.

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Contributing

Contributions are welcome.
Researchers and developers can improve QML-HCS by adding new modules, extending the documentation, or enhancing the quantum-hypercausal backends.

Contribution Guidelines

1. Fork the repository and create a feature branch.
2. Follow PEP 8 conventions and maintain typing annotations.
3. Ensure test coverage remains above 75%.
4. Provide detailed documentation and minimal runnable examples.
5. Submit a well-described pull request for review.

Further details are available in the Contributing Section.

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Testing and Documentation

To execute the test suite:

pytest -v

To build documentation locally:

sphinx-build -E -a -b html docs/ docs/_build/html

View the generated site by opening:

docs/_build/html/index.html

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Issues and Feedback

For bug reports or feature suggestions, please use the official issue tracker:

QML-HCS Issue Tracker

When reporting, include:

• Operating system and Python version
• Steps to reproduce
• Logs or traceback if available

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Research Vision

QML-HCS is part of the NeureonMindFlux Research Lab initiative to formalize quantum–causal computational frameworks.
It seeks to unify Quantum Machine Learning, Causal Inference, and Deterministic Modeling into a single, reproducible platform for scientific investigation and applied experimentation.

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Acknowledgments

Developed under the NeureonMindFlux Research Initiative in quantum-inspired and hypercausal computation.
The project benefits from ongoing collaboration and peer feedback within the open scientific community.

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📚 Documentation

Full documentation is available here:

QML-HCS Official Documentation

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Contact

For inquiries, collaboration proposals, or research-related communication regarding QML-HCS, please use the following contact:

Email: [e-mail]

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QML-HCS — advancing research in Quantum Machine Learning with Hypercausal Feedback Systems.

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