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 support@qognitive.io for information about commercial licensing to remove these limitations.
🛠️ Installation
pip install hone-io
Documentation available here
2 maintainers
Metadata
2 maintainers
Release files for hone-io 2.3.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hone_io-2.3.0-py3-none-manylinux_2_39_x86_64.whl | Python 3 | none | Linux glibc 2.39+ x86-64 | Details |
| hone_io-2.3.0-py3-none-manylinux_2_35_x86_64.whl | Python 3 | none | Linux glibc 2.35+ x86-64 | Details |
| hone_io-2.3.0-py3-none-macosx_15_0_arm64.whl | Python 3 | none | macOS 15.0+ ARM64 | Details |
Total release size: 8.7 MB
Release files / hone_io-2.3.0-py3-none-manylinux_2_39_x86_64.whl
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Release files / hone_io-2.3.0-py3-none-manylinux_2_35_x86_64.whl
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Release files / hone_io-2.3.0-py3-none-macosx_15_0_arm64.whl
| Download URL | hone_io-2.3.0-py3-none-macosx_15_0_arm64.whl |
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| Size | 1.8 MB |
| Tags | Python 3 macOS 15.0+ ARM64 |
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