QubeML

Educational notebooks for quantum computing and materials informatics. Six tool modules: Qiskit/Cirq/PennyLane for quantum algorithms, PyTorch/sklearn/Kwant for materials modeling.

Overview

Hands-on tutorials covering quantum algorithms for chemistry (VQE, quantum circuits) and ML for materials science (graph neural networks, property prediction). Built for grad students and researchers.

Modules

Quantum Computing - Qiskit: VQE for H2, HeH+ molecules - Cirq: Custom gates, noise simulation - PennyLane: Quantum kernels, variational classifiers

Materials Informatics - PyTorch: Crystal graph convolution networks - Scikit-learn: PCA on materials datasets, property regression - Kwant: 2D material transport, spin-orbit coupling

Topics Covered

| Module | Key Implementations | |--------|-----------------------| | Qiskit | VQE ground states, ansatz comparison, basis set effects | | PyTorch | CGCNN for band gaps, descriptor engineering | | Scikit-learn | Materials Project queries, feature importance | | Kwant | Graphene ribbons, MoS2 transistors | | Cirq | Error mitigation, qubit calibration | | PennyLane | Quantum embeddings, kernel methods |

Structure

quantum_computing/ qiskit/ # VQE tutorials, molecule examples cirq/ # Gate decomposition, error models pennylane/ # Quantum ML demos materials_informatics/ pytorch/ # GNN implementations scikit_learn/ # Classical ML pipelines kwant/ # Transport simulations src/ # Utilities (descriptors, plotting) tests/ # Unit tests

Setup

bash git clone https://github.com/meshalawein/QubeML.git cd QubeML python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate pip install -r requirements.txt

Google Colab: Notebooks work in Colab's free tier. Upload and run.

Notebooks

Quantum Chemistry (quantum_computing/qiskit/): - Build H2 molecule, run VQE with UCCSD ansatz - Compare to exact diagonalization - Basis set convergence study

Graph Neural Networks (materials_informatics/pytorch/): - Load crystal structures from CIF - Build graph representation - Train CGCNN on Materials Project data

Transport (materials_informatics/kwant/): - Graphene nanoribbon conductance - MoS2 field-effect transistor - Strain effects on band structure

ML Pipelines (materials_informatics/scikit_learn/): - Query MP API for oxide band gaps - Feature engineering from composition - Random forest vs gradient boosting comparison

Applications

These notebooks connect to active research areas: - Quantum advantage for strongly correlated molecules - ML-accelerated materials screening - Topological phases in 2D materials - Interpretable models for experimental validation

Testing

bash python -m pytest tests/ -v python tests/test_quantum_utils.py::TestQuantumUtils::test_bell_states

Contributing

Contributions welcome. See open issues or add examples/fixes via PR.

References

• Qiskit Textbook: https://qiskit.org/textbook/
• Materials Project: https://materialsproject.org/
• CGCNN paper: Xie & Grossman, Phys. Rev. Lett. 120, 145301 (2018)

License

MIT License. See LICENSE.

Author

Meshal Alawein
UC Berkeley
meshal@berkeley.edu