quantum-evolution-kernel 0.3.4

A Python library designed for the machine learning community to help users design quantum-driven similarity metrics for graphs and to use them inside kernel-based machine learning algorithms for graph data.ide the right environment to explore new ideas - both in terms of methodologies and data domain - while always interacting with a simple and intuitive QPU interface.

Quantum Evolution Kernel

The Quantum Evolution Kernel is a Python library designed for the machine learning community to help users design quantum-driven similarity metrics for graphs and to use them inside kernel-based machine learning algorithms for graph data.

The core of the library is focused on the development of a classification algorithm for molecular-graph dataset as it is presented in the published paper Quantum feature maps for graph machine learning on a neutral atom quantum processor(Journal Paper, arXiv).

Users setting their first steps into quantum computing will learn how to implement the core algorithm in a few simple steps and run it using the Pasqal Neutral Atom QPU. More experienced users will find this library to provide the right environment to explore new ideas - both in terms of methodologies and data domain - while always interacting with a simple and intuitive QPU interface.

Installation

Using hatch, uv or any pyproject-compatible Python manager

Edit file pyproject.toml to add the line

  "quantum-evolution-kernel"

to the list of dependencies.

Using pip or pipx

To install the pipy package using pip or pipx

1. Create a venv if that's not done yet

$ python -m venv venv

2. Enter the venv

$ . venv/bin/activate

3. Install the package

$ pip install quantum-evolution-kernel
# or
$ pipx install quantum-evolution-kernel

QuickStart

# Load a dataset
import torch_geometric.datasets as pyg_dataset
og_ptcfm = pyg_dataset.TUDataset(root="dataset", name="PTC_FM")

# Setup a quantum feature extractor for this dataset.
# In this example, we'll use QutipExtractor, to emulate a Quantum Device on our machine.
import qek.data.graphs as qek_graphs
import qek.data.extractors as qek_extractors
extractor = qek_extractors.QutipExtractor(compiler=qek_graphs.PTCFMCompiler())

# Add the graphs, compile them and look at the results.
extractor.add_graphs(graphs=og_ptcfm)
extractor.compile()
processed_dataset = extractor.run().processed_data

# Prepare a machine learning pipeline with Scikit Learn.
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC

X = [data for data in processed_dataset]  # Features
y = [data.target for data in processed_dataset]  # Targets
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify = y, test_size=0.2, random_state=42)

# Train a kernel
from qek.kernel import QuantumEvolutionKernel as QEK
kernel = QEK(mu=0.5)
model = SVC(kernel=kernel, random_state=42)
model.fit(X_train, y_train)

Documentation

We have a two parts tutorial:

1. Using a Quantum Device to extract machine-learning features;
2. Machine Learning with the Quantum Evolution Kernel

See also the full API documentation.

Getting in touch

• Pasqal Community Portal (forums, chat, tutorials, examples, code library).
• GitHub Repository (source code, issue tracker).
• Professional Support (if you need tech support, custom licenses, a variant of this library optimized for your workload, your own QPU, remote access to a QPU, ...)