pasqal-io/quantum-evolution-kernel
A Graph Machine Learning library using Quantum Computing
This library helps machine learning researchers and quantum computing enthusiasts develop quantum-driven similarity metrics for graph data. You input graph datasets, such as molecular structures, and it outputs results from quantum-enhanced kernel-based machine learning algorithms. It's designed for those exploring the intersection of quantum computing and graph-based machine learning.
No commits in the last 6 months. Available on PyPI.
Use this if you are a machine learning practitioner or quantum researcher wanting to experiment with quantum computing for graph classification tasks, especially with molecular graphs.
Not ideal if you are looking for a classical graph machine learning solution or are not interested in integrating quantum computing into your workflow.
Stars
56
Forks
13
Language
Python
License
—
Category
Last pushed
Sep 05, 2025
Commits (30d)
0
Dependencies
10
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/pasqal-io/quantum-evolution-kernel"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
PennyLaneAI/pennylane
PennyLane is an open-source quantum software platform for quantum computing, quantum machine...
qiskit-community/qiskit-machine-learning
An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum...
netket/netket
Machine learning algorithms for many-body quantum systems
tencent-quantum-lab/tensorcircuit
Tensor network based quantum software framework for the NISQ era
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum...