https://github.com/pasqal-io/quantum-evolution-kernel
A Graph Machine Learning library using Quantum Computing
https://github.com/pasqal-io/quantum-evolution-kernel
graph-machine-learning graphs machine-learning molecule quantum-computing
Last synced: 10 months ago
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A Graph Machine Learning library using Quantum Computing
- Host: GitHub
- URL: https://github.com/pasqal-io/quantum-evolution-kernel
- Owner: pasqal-io
- License: other
- Created: 2024-12-09T16:23:29.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-04T13:21:48.000Z (10 months ago)
- Last Synced: 2025-04-04T14:26:49.851Z (10 months ago)
- Topics: graph-machine-learning, graphs, machine-learning, molecule, quantum-computing
- Language: Python
- Homepage: https://pasqal-io.github.io/quantum-evolution-kernel/latest/
- Size: 3.3 MB
- Stars: 46
- Watchers: 6
- Forks: 5
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: docs/contributing/first contact.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://pypi.org/project/quantum-evolution-kernel/)
[](https://github.com/pasqal-io/quantum-evolution-kernel/actions/workflows/test.yml)
# 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](https://journals.aps.org/pra/abstract/10.1103/PhysRevA.107.042615), [arXiv](https://arxiv.org/abs/2107.03247)).
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
```sh
$ python -m venv venv
```
2. Enter the venv
```sh
$ . venv/bin/activate
```
3. Install the package
```sh
$ pip install quantum-evolution-kernel
# or
$ pipx install quantum-evolution-kernel
```
## QuickStart
```python
# 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](https://pasqal-io.github.io/quantum-evolution-kernel/v0.3.1/tutorial%201%20-%20Using%20a%20Quantum%20Device%20to%20Extract%20Machine-Learning%20Features);
2. [Machine Learning with the Quantum Evolution Kernel](https://pasqal-io.github.io/quantum-evolution-kernel/v0.3.1/tutorial%202%20-%20Machine-Learning%20with%20the%20Quantum%20EvolutionKernel/)
See also the [full API documentation](https://pasqal-io.github.io/quantum-evolution-kernel/v0.3.1/).
## Getting in touch
- [Pasqal Community Portal](https://community.pasqal.com/) (forums, chat, tutorials, examples, code library).
- [GitHub Repository](https://github.com/pasqal-io/quantum-evolution-kernel) (source code, issue tracker).
- [Professional Support](https://www.pasqal.com/contact-us/) (if you need tech support, custom licenses, a variant of this library optimized for your workload, your own QPU, remote access to a QPU, ...)