quantum-learn 2.0.0

quantum-learn: quantum machine learning in Python

quantum-learn

quantum-learn is a quantum machine learning library with backend-specific APIs for Pennylane and Qiskit, plus higher-level estimators and VQC wrappers for common workflows.

Features

• Backend-specific entry points such as qlearn.pennylane.QuantumFeatureMap and qlearn.qiskit.QuantumFeatureMap
• A generic VariationalQuantumCircuit with configurable measurement and loss settings
• Task-oriented VariationalQuantumClassifier and VariationalQuantumRegressor wrappers with sensible defaults
• Hybrid estimators for classification, regression, and clustering on top of quantum feature maps
• Optional backend dependencies so importing qlearn does not require every quantum framework
• A default top-level backend for users who want a simple qlearn.QuantumFeatureMap() or qlearn.VariationalQuantumCircuit() entry point

Installation

Base install:

pip install quantum-learn

The base install is enough to import the package and use backend-independent utilities, but any quantum execution requires at least one backend extra.

Install with a specific backend:

pip install "quantum-learn[pennylane]"
pip install "quantum-learn[qiskit]"

Install both backends:

pip install "quantum-learn[all]"

Install from source:

git clone https://github.com/OsamaMIT/quantum-learn.git
cd quantum-learn
pip install -e ".[pennylane]"

Backend Model

• qlearn.pennylane exposes the implemented Pennylane backend.
• qlearn.qiskit currently exposes the Qiskit QuantumFeatureMap.
• The top-level qlearn.QuantumFeatureMap and qlearn.VariationalQuantumCircuit resolve to the default backend, which is currently Pennylane.
• qlearn.qiskit.VariationalQuantumCircuit is intentionally not exported yet because it is not implemented.

Usage

Use a backend directly:

from qlearn.pennylane import QuantumFeatureMap

qfm = QuantumFeatureMap()
transformed = qfm.transform(data)

Use a hybrid estimator with an explicit backend:

from qlearn import HybridClassification

model = HybridClassification(backend="pennylane")
model.fit(features, labels)
predictions = model.predict(features)

Use clustering with a sklearn-style workflow:

from qlearn import HybridClustering

clusterer = HybridClustering(backend="pennylane")
labels = clusterer.fit_predict(features, n_clusters=3)

Use a task wrapper on top of the shared VQC:

from qlearn import VariationalQuantumClassifier, VariationalQuantumRegressor

classifier = VariationalQuantumClassifier()
classifier.fit(features, labels)
predictions = classifier.predict(features)
probabilities = classifier.predict_proba(features)

regressor = VariationalQuantumRegressor()
regressor.fit(features, targets)
values = regressor.predict(features)

Or use the generic VQC directly with explicit output and loss choices:

from qlearn import VariationalQuantumCircuit

vqc = VariationalQuantumCircuit()
vqc.fit(
    features,
    labels,
    measurement="probabilities",
    loss="cross_entropy",
)
raw_outputs = vqc.predict(features)

You can also override the task defaults by supplying custom target encoders, prediction decoders, probability decoders, ansatz functions, or fit-time VQC options.

API Notes

• fit() is the primary training method. train() remains available as an alias for compatibility.
• HybridClustering.predict() now requires a fitted model. Use fit_predict() when you want clustering in a single call.
• VariationalQuantumClassifier defaults to probability outputs and cross-entropy training, with automatic label encoding and predict_proba().
• VariationalQuantumRegressor defaults to expectation-value outputs and MSE training, with automatic target scaling and inverse-scaling at prediction time.
• VariationalQuantumCircuit now supports configurable measurement, measurement_wires, and loss values in fit().
• Both wrappers still accept custom target encoders and decoders when you want full control.

Documentation

For tutorials, examples, and details on the classes, check out the quantum-learn documentation.

Dependencies

If you're working with the source, the required dependencies can be installed by

pip install -r requirements.txt

Planned Features

• Implement quantum kernel methods
• Implement categorical feature maps

Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a new branch (feature-branch)
3. Commit your changes and open a pull request

License

This project is licensed under the MIT License. See LICENSE for details.