qml-pennylane 0.2.3

Modular PennyLane-based quantum machine learning suite for classification, regression, and quantum kernel methods.

Quantum Machine Learning

PyPI: https://pypi.org/project/qml-pennylane/

Web pages: https://SidRichardsQuantum.github.io/Quantum_Machine_Learning/

Modular PennyLane-based quantum machine learning library implementing reusable workflows for:

• Variational quantum classification (VQC)
• Variational quantum regression (VQR)
• Quantum convolutional neural networks (QCNN)
• Quantum autoencoders
• Quantum kernel methods
• Dataset-agnostic estimator APIs for user-supplied arrays
• Quantum kernel classification and regression
• Trainable quantum kernels (kernel-target alignment)
• Multi-output variational regression and multiclass classification
• Time-series windowing utilities
• Human-readable reporting tables for notebooks and CLIs
• Quantum metric learning (trainable embedding geometry)
• Classical baseline models
• Deterministic benchmark utilities

The repository follows a package-first design:

• algorithms implemented in src/qml/
• notebooks act as thin clients
• experiments produce reproducible outputs
• consistent plotting and result structures
• deterministic execution via explicit seeds
• implementation contracts document the circuit/model family, objective, and metric semantics for each advertised algorithm

---

Table of Contents

• Installation

• Quick start

  • Variational quantum classifier
  • Variational quantum regression
  • Quantum convolutional neural network
  • Quantum autoencoder
  • Quantum kernel classifier
  • Dataset-agnostic estimators
  • Trainable quantum kernel
  • Quantum metric learning

• Noise-aware execution (finite shots)

• Benchmark framework

• Model-specific configuration

• Classical baselines

• Command line interface

• Results

• Documentation

• Repository structure

• Design principles

  • Package-first architecture
  • Deterministic workflows
  • Minimal abstractions

• Current algorithms

  • Variational quantum classifier
  • Variational quantum regression
  • Quantum kernel classifier
  • Trainable quantum kernel
  • Quantum metric learning

• Development workflow

• Support development

• Author

• License

---

Installation

Clone and install in editable mode:

pip install -e .

Install development tools:

pip install -e ".[dev]"

Requirements:

• Python ≥ 3.10
• PennyLane ≥ 0.34
• NumPy ≥ 1.24
• scikit-learn ≥ 1.3
• matplotlib ≥ 3.7

---

Quick start

Variational quantum classifier

from qml.classifiers import run_vqc

result = run_vqc(
    n_samples=200,
    n_layers=2,
    steps=50,
    plot=True,
)

---

Variational quantum regression

from qml.regression import run_vqr

result = run_vqr(
    n_samples=200,
    n_layers=2,
    steps=50,
    plot=True,
)

---

Quantum convolutional neural network

from qml.qcnn import run_qcnn

result = run_qcnn(
    n_samples=200,
    steps=50,
    plot=True,
)

Learns a small hierarchical quantum classifier using:

• trainable data embedding across four qubits
• shared convolution-style two-qubit blocks
• pooling-style entangling reductions before final readout

---

Quantum autoencoder

from qml.autoencoder import run_quantum_autoencoder

result = run_quantum_autoencoder(
    n_samples=200,
    family="correlated",
    steps=50,
    plot=True,
)

Learns a compression map for structured four-qubit state families using:

• a trainable encoder/decoder ansatz
• a latent subspace retained across selected qubits
• compression and reconstruction fidelity metrics

---

Quantum kernel classifier

from qml.kernel_methods import run_quantum_kernel_classifier

result = run_quantum_kernel_classifier(
    n_samples=200,
    plot=True,
)

---

Dataset-agnostic estimators

Use these APIs when you already have application data, for example features from a physics simulator, sensor pipeline, spectrum, graph descriptor, or time-series window.

import numpy as np

from qml import (
    QuantumClassifier,
    QuantumKernel,
    QuantumKernelClassifier,
    QuantumKernelRegressor,
    QuantumRegressor,
    make_sequence_windows,
)

# X and y can come from any domain-specific simulator or data source.
X = np.asarray([[0.0, 0.1], [0.2, 0.0], [1.0, 0.9], [0.8, 1.0]])
y_class = np.asarray([0, 0, 1, 1])
y_reg = np.asarray([0.1, 0.2, 0.9, 0.8])

kernel = QuantumKernel(seed=0)
clf = QuantumKernelClassifier(kernel).fit(X, y_class)
reg = QuantumKernelRegressor(kernel, alpha=1e-3).fit(X, y_reg)

vqc = QuantumClassifier(n_layers=1, steps=10, seed=0).fit(X, y_class)
vqr = QuantumRegressor(n_layers=1, steps=10, seed=0).fit(X, y_reg)

series_X, series_y = make_sequence_windows(np.arange(10), window_size=3)

These estimators keep the package general: the package handles circuits, kernels, training, and metrics, while users supply domain-specific features and targets.

---

Human-readable tables

Use reporting helpers for compact notebook or CLI output:

from qml.reporting import print_table

print_table(
    [("Quantum MAE", 0.086147), ("Baseline MAE", 0.020856)],
    title="Results",
)

---

Trainable quantum kernel (kernel-target alignment)

from qml.trainable_kernels import run_trainable_quantum_kernel_classifier

result = run_trainable_quantum_kernel_classifier(
    n_samples=200,
    steps=50,
    plot=True,
)

---

Quantum metric learning

from qml.metric_learning import run_quantum_metric_learner

result = run_quantum_metric_learner(
    samples=200,
    layers=2,
    steps=50,
    plot=True,
)

Learns a trainable embedding circuit using contrastive supervision:

• same-class samples mapped closer together
• different-class samples separated in feature space

Classification is performed via nearest-centroid prediction in the learned embedding.

---

Workflows return structured result objects containing training metrics, predictions, learned parameters, and configuration metadata. Most APIs return dictionaries; the metric-learning workflow returns a typed dataclass.

---

Noise-aware execution (finite shots)

Quantum circuits can be evaluated either analytically or with finite sampling.

Finite-shot execution uses:

qml.set_shots(qnode, shots)

Example:

result = run_vqc(
    n_samples=200,
    n_layers=2,
    steps=50,
    shots=128,
)

Trainable kernel workflows support separate shot settings:

result = run_trainable_quantum_kernel_classifier(
    n_samples=200,
    shots_train=64,
    shots_kernel=256,
)

All workflows remain deterministic when a fixed seed is provided.

---

Benchmark framework

Benchmark utilities compare quantum and classical models across multiple seeds.

Example:

from qml.benchmarks import compare_classification_models

result = compare_classification_models(
    models=[
        "vqc",
        "qcnn",
        "quantum_kernel",
        "trainable_quantum_kernel",
        "logistic_regression",
        "svm_classifier",
    ],
    seeds=[123, 456],
)

---

Model-specific configuration

Benchmarks accept per-model kwargs:

result = compare_classification_models(
    models=[
        "vqc",
        "qcnn",
        "quantum_kernel",
        "trainable_quantum_kernel",
    ],
    seeds=[123],
    model_kwargs={
        "vqc": {"shots": 128},

        "quantum_kernel": {"shots": 256},

        "trainable_quantum_kernel": {
            "shots_train": 64,
            "shots_kernel": 256,
        },
    },
)

Result structure remains consistent across models. Benchmark summaries include aggregate train/test metrics, runtime summaries, generalization-gap summaries, and a best_model convenience field based on the primary test metric. Use these summaries with classical baselines and multiple seeds; the smoke-scale defaults are for reproducibility checks, not quantum-advantage claims.

---

Classical baselines

Included reference models:

• logistic regression
• ridge regression
• support vector machine
• multilayer perceptron

These provide performance context for quantum models.

---

Command line interface

Run workflows directly:

python -m qml vqc --steps 50 --plot
python -m qml qcnn --steps 50 --plot
python -m qml autoencoder --steps 50 --plot
python -m qml regression --steps 50 --plot
python -m qml kernel --plot
python -m qml trainable-kernel --steps 50 --plot
python -m qml metric-learning --steps 50 --plot

Run benchmarks:

python -m qml benchmark classification \
    --models vqc qcnn quantum_kernel svm_classifier logistic_regression \
    --seeds 123 456

python -m qml benchmark regression \
    --models vqr ridge_regression mlp_regressor \
    --seeds 123 456

CLI outputs include:

• training metrics
• test metrics
• final loss
• saved plots (optional)

---

Results

Reference results and notebook result pages are generated from one script:

python docs/pages/generate_results.py

The generated outputs are:

• RESULTS.md — smoke-scale API reference results
• RESULTS_TUTORIALS.md — tables and plots extracted from tutorial notebooks
• RESULTS_REAL_EXAMPLES.md — tables and plots extracted from real-example notebooks

Pass --execute-notebooks to rerun notebooks before extracting notebook outputs. The GitHub Pages workflow uses this mode so the published result pages stay aligned with the notebooks. These are reproducible reference outputs, not quantum-advantage claims.

---

Documentation

Published documentation:

• https://SidRichardsQuantum.github.io/Quantum_Machine_Learning/

Core documentation:

• THEORY.md — mathematical background
• USAGE.md — API examples
• docs/qml/api_reference.md — public imports, workflows, estimators, benchmarks, and helpers
• RESULTS.md — generated deterministic reference outputs
• RESULTS_TUTORIALS.md — generated tutorial notebook outputs
• RESULTS_REAL_EXAMPLES.md — generated real-example notebook outputs

Algorithm notes:

• docs/qml/api_reference.md
• docs/qml/variational_quantum_classifier.md
• docs/qml/variational_regression.md
• docs/qml/qcnn.md
• docs/qml/autoencoder.md
• docs/qml/quantum_kernels.md
• docs/qml/metric_learning.md

Tutorial notebooks:

• notebooks/tutorials/01-classical-vs-quantum-classifier.ipynb
• notebooks/tutorials/02-classical-vs-quantum-regressor.ipynb
• notebooks/tutorials/03-variational-quantum-classifier.ipynb
• notebooks/tutorials/04-variational-quantum-regressor.ipynb
• notebooks/tutorials/05-quantum-kernel-classifier.ipynb
• notebooks/tutorials/06-quantum-kernel-estimators.ipynb
• notebooks/tutorials/07-variational-quantum-estimators.ipynb
• notebooks/tutorials/08-sequence-window-quantum-forecasting.ipynb
• notebooks/tutorials/09-quantum-metric-learning.ipynb
• notebooks/tutorials/10-quantum-convolutional-neural-network.ipynb
• notebooks/tutorials/11-quantum-autoencoder.ipynb

---

Repository structure

src/

  qml/

    ansatz.py
        parameterised circuit templates

    embeddings.py
        feature encoding circuits

    classifiers.py
        variational quantum classification workflows

    regression.py
        variational quantum regression workflows

    qcnn.py
        quantum convolutional classifier workflows

    autoencoder.py
        quantum autoencoder workflows

    kernel_methods.py
        quantum kernel workflows

    trainable_kernels.py
        kernel-target alignment optimisation

    metric_learning.py
        contrastive quantum embedding optimisation

    estimators.py
        dataset-agnostic variational estimator APIs

    kernels.py
        reusable quantum kernel estimator APIs

    preprocessing.py
        sequence windowing and preprocessing helpers

    reporting.py
        human-readable result tables for notebooks and CLIs

    classical_baselines.py
        logistic, ridge, svm, mlp

    benchmarks.py
        multi-seed benchmark utilities

    training.py
        hybrid optimisation loops

    metrics.py
        evaluation metrics

    losses.py
        objective functions

    data.py
        dataset generation utilities

    visualize.py
        plotting utilities

    io_utils.py
        reproducible saving utilities


notebooks/

    tutorials/
        algorithm and implementation walkthroughs

    real_examples/
        small reproducible domain examples


tests/

    smoke tests
    deterministic benchmarks


docs/

    theory notes and algorithm descriptions


results/

    saved experiment outputs (gitignored)


images/

    generated plots (gitignored)

---

Design principles

Package-first architecture

Core implementations live in src/qml/ and are imported as:

qml.*

Notebooks import public APIs rather than defining circuits inline.

---

Deterministic workflows

Reproducibility is prioritised:

• explicit random seeds
• deterministic dataset generation
• reproducible optimisation
• consistent JSON outputs
• deterministic finite-shot execution

---

Minimal abstractions

Shared infrastructure intentionally remains lightweight:

• small set of embeddings
• hardware-efficient ansatz
• simple optimisation loops
• consistent plotting utilities

---

Current algorithms

Variational quantum classifier

Binary classification using:

• angle embedding
• hardware-efficient ansatz
• cross-entropy loss

---

Variational quantum regression

Continuous prediction using:

• angle embedding
• expectation-value outputs
• mean squared error

---

Quantum kernel classifier

Support vector machine using quantum feature maps:

$$ K(x_i, x_j)

|\langle \phi(x_i) | \phi(x_j) \rangle|^2 $$

---

Trainable quantum kernel

Kernel alignment objective:

$$ \max_\theta ; \frac{ \langle K_\theta, Y \rangle_F }{ |K_\theta|_F |Y|_F } $$

where:

• $K_\theta$ is the quantum kernel matrix
• $Y$ is the label similarity matrix

---

Quantum metric learning

Supervised embedding optimisation using contrastive loss:

$$ L = y d^2 + (1 - y)\max(0, m - d)^2 $$

where:

• $d$ is distance between learned embeddings
• $y \in {0,1}$ indicates whether samples share a class
• $m$ is a separation margin

The learned embedding is used for classification via nearest-centroid prediction in feature space.

Supports:

• trainable data re-uploading embeddings
• stochastic pair sampling
• deterministic optimisation via fixed seeds
• consistent evaluation pipeline with other models

---

Development workflow

Run tests:

pytest

Format code:

black .
ruff check .

Run module:

python -m qml

---

Support development

If this project is useful for research, learning, or experimentation, you can support continued development via GitHub Sponsors:

https://github.com/sponsors/SidRichardsQuantum

Sponsorship supports continued work on open-source implementations of quantum machine learning models, including improvements to documentation, reproducible experiments, benchmark utilities, and example workflows.

Support helps maintain accessible implementations of variational quantum models, quantum kernels, and hybrid quantum–classical learning tools.

---

Author

Sid Richards

LinkedIn: https://www.linkedin.com/in/sid-richards-21374b30b/

GitHub: https://github.com/SidRichardsQuantum

---

License

MIT License — see LICENSE