quantum-ml-comparator 0.2.1

Compare quantum ML algorithms against classical ML — with automatic QML recommendations

quantum-ml-comparator

Developed at Orbion GmbH.

Compare quantum machine learning algorithms against classical ML — with automatic QML recommendations.

A general-purpose, open-source framework to benchmark QML vs classical ML on your own datasets. Tell it what classical algorithm you're using, and it recommends which quantum algorithms to compare against, explains why, and runs the comparison for you.

Install

pip install -e .

For molecular VQE support:

pip install -e ".[molecules]"

Quickstart (3 lines)

from qmc import Benchmark

bench = Benchmark(dataset="iris", classical_methods=["MLP", "SVM", "RF"])
bench.run()
bench.report("results/")

Quantum methods are auto-recommended based on your classical methods.

The QML Recommender

Don't know which quantum algorithm to try? Ask:

from qmc import print_recommendations

print_recommendations("RandomForest")

Output:

[PRIMARY] Quantum Kernel Ensemble  (difficulty: medium)
  Ensemble of quantum-kernel SVMs on bootstrap samples, mimicking Random Forest's bagging.
  Rationale: Combining multiple quantum kernel models reduces variance, similar to how
             Random Forest aggregates decision trees.
  Circuit:   8 qubits, 4 layers

[SECONDARY] VQC  (difficulty: easy)
  Variational Quantum Classifier as a single strong learner replacing the tree ensemble.
  Rationale: A sufficiently expressive VQC can match an ensemble of weak learners.

Supported classical algorithms: SVM, MLP, Random Forest, Logistic Regression, k-NN, XGBoost, Naive Bayes, PCA (plus any algorithm falls back to general-purpose VQC/QuantumKernel).

What's Included

Classical models

MLP (PyTorch), SVM, Random Forest, Logistic Regression, k-NN, Gradient Boosting, Naive Bayes, Decision Tree.

Quantum circuits

• VQC — Variational Quantum Classifier (binary + multiclass)
• Quantum Kernel — IQP-style feature map + precomputed SVM
• QNP ansatz — Particle-number-preserving gates (Anselmetti et al.)
• HEA ansatz — StronglyEntanglingLayers (generic)
• Plus circuit factory templates for custom designs

Molecular VQE

Run VQE on standard benchmark molecules (H2, HeH+, LiH, H2O) with QNP or HEA ansatze. Compare ansatz performance:

from qmc.molecules import VQERunner

runner = VQERunner(molecule="H2", ansatz="QNP", n_layers=4)
result = runner.run()
print(f"Energy: {result.energy:.6f} Ha  Error: {result.error:.2e}")

Live dashboard

from qmc.dashboard import start_dashboard
start_dashboard(port=8501)
# Open http://localhost:8501 — live training curves during bench.run()

Bring Your Own Data

import numpy as np
from qmc import Benchmark

X = np.random.randn(500, 6)
y = (X[:, 0] * X[:, 1] > 0).astype(int)

bench = Benchmark(dataset=(X, y), classical_methods=["RF", "MLP"])
bench.run()

Or from a CSV:

bench = Benchmark(dataset="data.csv", target_column="label")

Built-in datasets: iris, breast_cancer, wine, digits, moons, circles, blobs.

Example Output

Running examples/01_quickstart.py on Iris:

Rank	Method	Type	Accuracy	F1	Time
1	VQC	quantum	1.0000	1.0000	340.7s
2	QuantumKernel	quantum	0.9556	0.9554	7.8s
3	MLP	classical	0.9333	0.9333	0.06s
4	SVM	classical	0.9111	0.9107	0.001s
5	RF	classical	0.8889	0.8878	0.03s

Examples

• examples/01_quickstart.py — Classical vs quantum on Iris
• examples/02_recommender.py — Get QML recommendations
• examples/03_molecule_vqe.py — VQE on H2 (requires PySCF)
• examples/04_custom_dataset.py — Bring your own numpy data

Run Tests

pip install -e ".[dev]"
pytest tests/

Mappings Reference

Your classical algorithm	Recommended quantum counterpart
SVM	Quantum Kernel SVM, VQC
MLP / Neural Net	VQC, Data Re-uploading VQC
Random Forest	Quantum Kernel Ensemble, VQC
Logistic Regression	Quantum Kernel + Linear SVM
k-NN	Quantum Kernel k-NN
XGBoost	Quantum Kernel SVM, Quantum Boosted Ensemble
Naive Bayes	VQC with probabilistic readout
PCA	Quantum feature map, Quantum Autoencoder
anything else	VQC, Quantum Kernel (general-purpose)

License

MIT — use it anywhere.

Acknowledgments

This repository was developed with AI coding assistance. The research direction, experimental design, verification, and technical decisions are original work; the code scaffolding was accelerated with Claude.

QNP gate implementation based on Anselmetti et al. (2021). Built on PennyLane and scikit-learn.