hone-io 2.3.0

Library for quantum cognition machine learning.

Hone-io Community Edition - QCML Scikit-Learn Integration

Welcome to the Honeio Community Edition! This package provides scikit-learn compatible wrappers for Quantum Cognition Machine Learning (QCML) models. Documentation available here.

🚀 What is QCML?

Quantum Cognition Machine Learning is a new form of machine learning that is inspired by quantum cognition. QCML models learn a representation of the input data into quantum states, and the outputs of the models reflect the outcomes of quantum measurements. QCML is highly effective on datasets with a large number of input features and a large number of classes (for classification) or targets (for regression).

For more details you can check out some of our publications:

• Quantum Cognition Machine Learning AI Needs Quantum
• Robust estimation of the intrinsic dimension of data sets with quantum cognition machine learning
• Quantum Cognition Machine Learning: Financial Forecasting
• Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning
• Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
• Quantum Geometry of Data
• QCML Patient Similarity - UTI Prediction
• Breaking the curse of dimensionality: QCML vs. tree-based models

📦 Available Classes

QCMLRegressor

A scikit-learn compatible regressor for continuous target prediction tasks.

QCMLClassifier

A scikit-learn compatible classifier for discrete classification tasks.

Both classes wrap the underlying QCML layers with scikit-learn wrapper and provide familiar scikit-learn interfaces.

🎯 Key Features

• Scikit-learn compatibility: Drop-in replacement for sklearn estimators
• Quantum-inspired learning: Represents data with quantum states
• Adaptive weighting: Learnable input feature weights for automatic feature selection
• GPU support: Train on CPU or CUDA devices
• Model persistence: Save and load trained models
• Flexible batching: Support for batch training or full-batch optimization

⚠️ Community Edition Limitations

The community edition has the following restrictions:

Parameter	Limit	Description
Input Features	100	Maximum number of input features/operators
Output Features	12	Maximum number of output features/operators
Hilbert Space Dimension	8	Maximum dimension of the quantum state space
Training Samples	1,000	Maximum number of training samples per batch

💡 Need more capacity? Contact [e-mail] for information about commercial licensing to remove these limitations.

🛠️ Installation

pip install hone-io

Documentation available here