Qubeml

Qubeml is a collection of educational Jupyter notebooks for quantum computing and materials informatics, organized into six tool modules. Each module provides hands-on tutorials and working code examples designed for graduate students and researchers entering these fields. Google Colab support is included for all notebooks, enabling execution without local environment setup.

The Qiskit module covers variational quantum eigensolver (VQE) algorithms and molecular ground state calculations. The Cirq module focuses on error mitigation techniques for noisy intermediate-scale quantum (NISQ) devices. The PennyLane module introduces quantum kernel methods for machine learning classification tasks. On the materials informatics side, the PyTorch module implements crystal graph neural networks for predicting material properties from atomic structure data. The scikit-learn module demonstrates data pipelines using the Materials Project database for materials screening and property prediction. The Kwant module provides quantum transport simulations for graphene and MoS2 nanostructures.

Qubeml connects to several related projects. It is linked to qmatsim, which performs multiscale strain engineering simulations on the same TMD materials (MoS2, WS2, and others) studied in the Kwant transport module. The qmlab project provides a web-based React/TypeScript interface for quantum ML experiments, complementing the notebook-based approach in qubeml. The scicomp framework references qubeml as part of its cross-platform quantum simulation ecosystem, which includes equivalent implementations in Python, MATLAB, and Mathematica. The repository is hosted at github.com/alawein/qubeml and is in active development in the quantum-computing domain at P3 priority.