tinkueg/quask

How tinkueg/quask works

QuASK is a Python quantum-machine-learning toolkit whose main workflow retrieves data, preprocesses and splits it, computes classical or quantum Gram matrices, then evaluates kernel performance. The CLI orchestrates these stages; the library also exposes kernel registers, PennyLane circuit templates, trainable kernels, projected kernels, and metric calculations. The README documents additional analysis capabilities, including prediction bounds and observable-based studies, but the excerpts do not show their full implementation. Edges use the README or source-index relationships where available; undocumented internals are not inferred.

Main entry points: __main__.py QuASK CLI · template_pennylane.py Quantum kernels · datasets.py Dataset registry · kernels.py Kernel registry

Components 19

Data workflow

QuASK CLI quask/__main__.py
Dataset registry quask/datasets.py
OpenML datasets
Bundled quantum data quask/resources
Preprocessing quask/__main__.py
Train/test data quask/__main__.py

Kernel computation

Kernel registry quask/kernels.py
Classical kernels quask/kernels.py
Quantum kernels quask/template_pennylane.py
Projected kernels quask/template_pennylane.py
Gram matrices quask/__main__.py

Kernel analysis

Kernel metrics quask/metrics.py
Performance assessment quask/__main__.py
Advantage bounds quask

Quantum models

Circuit templates quask/template_pennylane.py
Trainable kernels quask/template_pennylane.py
Observable studies quask

Other components

Researcher
Quantum frameworks

Connections 22

Researcher → QuASK CLI: runs commands README.md
QuASK CLI → Dataset registry: retrieves datasets quask/__main__.py
Dataset registry → OpenML datasets: downloads data quask/datasets.py
Dataset registry → Bundled quantum data: loads data quask/datasets.py
QuASK CLI → Preprocessing: preprocesses data README.md
Preprocessing → Train/test data: splits data quask/__main__.py
QuASK CLI → Kernel registry: selects kernels quask/__main__.py
Kernel registry → Classical kernels: registers kernels quask/kernels.py
Kernel registry → Quantum kernels: registers kernels quask/kernels.py
Quantum kernels → Circuit templates: uses templates quask/kernels.py
Quantum kernels → Projected kernels: supports projection quask/kernels.py
QuASK CLI → Trainable kernels: optimizes circuits quask/__main__.py
Trainable kernels → Circuit templates: builds circuits quask/template_pennylane.py
Classical kernels → Gram matrices: computes matrices README.md
Quantum kernels → Gram matrices: computes matrices quask/__main__.py
Trainable kernels → Gram matrices: returns matrices quask/template_pennylane.py
QuASK CLI → Kernel metrics: evaluates metrics quask/__main__.py
Kernel metrics → Performance assessment: assesses performance README.md
Gram matrices → Performance assessment: provides inputs README.md
Kernel metrics → Advantage bounds: supports bounds README.md
Circuit templates → Observable studies: supports observables README.md
Circuit templates → Quantum frameworks: uses PennyLane quask/template_pennylane.py

Drawn by GitDiagram on November 12, 2026 from the repository's file tree, README and source files. Read as Markdown ·