QCML Documentation

Welcome to QCML (Quantum Cognition Machine Learning), a powerful library for quantum machine learning developed by Qognitive, Inc.

Contents:

• Scikit-learn Integration
  • Example Usage
• Integrations
  • Scikit-learn Integration
    • Classes
    • Core Estimators
      • QCMLRegressor
      • QCMLClassifier
    • Utilities and Hooks
      • Serialization Functions
    • Usage Examples
      • Basic Regression Example
      • Basic Classification Example
      • Model Persistence Example
      • Integration with Scikit-learn Pipelines
    • Parameter Configuration
      • Model Parameters
      • Advanced Configuration
    • Compatibility
    • Performance Tips
    • Troubleshooting
  • Overview
• Examples
  • Intro to QCML
    • Basic Usage
    • Example Code
    • Expected Output
    • Understanding the Results
    • Key Parameters
    • Next Steps
  • Binary Classification
    • Overview
    • Complete Example
    • Dataset Information
    • Cross-Validation Setup
    • Training Output
    • Results Analysis
    • Performance Results
    • Key Findings
    • Best Practices Demonstrated
    • Next Steps
  • Multiclass Classification
    • Overview
    • Complete Example
    • Dataset Information
    • Community Edition Considerations
    • Cross-Validation Setup
    • Training Output
    • Results Analysis
    • Performance Results
    • Key Findings
    • Configuration Insights
    • Best Practices Demonstrated
    • Next Steps
  • Regression
    • Overview
    • Complete Example
    • Dataset Information
    • Cross-Validation Setup
    • Advanced Configuration
    • Training Output
    • Results Analysis
    • Performance Results
    • Key Findings
    • Metric Interpretation
    • Configuration Insights
    • Best Practices Demonstrated
    • Next Steps
  • GPU vs CPU Benchmark
    • Overview
    • Complete Example
    • Benchmark Setup
    • Training Output
    • Performance Results
    • Key Findings
    • Performance Considerations
    • Hardware Requirements
    • Best Practices
    • Code Pattern for Device Selection
    • Performance Scaling
    • Next Steps

Installation

QCML is available in two editions:

Community Edition

pip install hone-io

Enterprise Edition

pip install hone-io-enterprise

For the enterprise edition, you need a valid license that can be requested at https://www.qognitive.io/api-request/

Quick Start

QCML provides seamless integration with popular machine learning frameworks. The sklearn integration allows you to use quantum-enhanced models with the familiar scikit-learn API.

from honeio.integrations import QCMLRegressor

# Create and train a quantum-enhanced regressor
model = QCMLRegressor(hilbert_space_dim=16, epochs=100)
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)

For complete working examples, see the Examples section.

Features

• Scikit-learn Integration: Drop-in replacement for traditional ML models

• Quantum Enhancement: Leverages quantum computing principles for improved performance

• Easy to Use: Familiar API for seamless adoption

• Flexible Architecture: Customizable quantum layer configurations

• Complete ML Coverage: Support for classification and regression tasks

• GPU Acceleration: Significant performance improvements with CUDA support

AI Assistant Integration

For AI coding assistants (like Cursor), a comprehensive API reference is available in CURSOR_DOCS.md - a markdown synthesis of this documentation optimized for AI consumption.

License

This software is licensed under a proprietary license. See the LICENSE file for details.

Indices and tables

• Index

• Module Index

• Search Page