Applications#

QBioCode provides standalone applications for common quantum machine learning workflows. These apps offer user-friendly interfaces and configuration-based workflows for complex analyses.

QProfiler#

QProfiler is an automated benchmarking tool for comparing quantum and classical machine learning models. It provides:

  • Systematic model evaluation across multiple algorithms

  • YAML-based configuration for reproducible experiments

  • Automated performance metrics collection (accuracy, F1-score, AUC)

  • Statistical analysis and visualization tools

  • Support for custom datasets and embeddings

See the QProfiler documentation for detailed usage instructions.

QSage#

QSage is an intelligent meta-learning system that predicts which machine learning models will perform best on your dataset before you run them. By learning from data complexity patterns across multiple datasets, QSage provides data-driven model recommendations.

  • Learns from History: Trains on data complexity metrics and model performance from previous experiments

  • Predicts Performance: Estimates how well each model will perform on new, unseen datasets

  • Ranks Models: Provides confidence-weighted rankings of classical and quantum models

  • Saves Time: Helps you focus computational resources on the most promising models

See the QSage documentation for detailed usage instructions.

QuVINE#

QuVINE (Quantum View-based Network Embeddings) turns a graph into low-dimensional node embeddings, combining classical and quantum random walks with SGNS-based representation learning. It is provided as an in-tree app (qbiocode.apps.quvine) plus a quvine command-line tool.

  • Multi-view graph construction from a single input graph

  • Classical and quantum random walks (RWR, discrete- and continuous-time quantum walks)

  • SGNS-based embedding learning, with quantum-calibrated filter / GAT / GraphGPS variants

  • Classical baselines (node2vec, NetMF, APPNP) for comparison

  • Reproducible, iterative evaluation pipelines

  • 83 named methods selectable by a single method string

  • Usable through qbiocode.get_embeddings() alongside pca, nmf and umap

QuVINE’s dependencies are optional, so install it with pip install "qbiocode[quvine]".

Graph-complexity metrics are intentionally not part of the embedding app; they live in qbiocode.evaluate_graph() (qbiocode.evaluation.graph_evaluation). All 88 of them are grouped and described under Graph-Complexity Measures.

See the QuVINE documentation for detailed usage instructions, and the QuVINE Configuration Guide for the shipped YAML config.

On this page