Algorithm Documentation Coverage¶

This page maps the implemented QML algorithm surfaces to their theory notes, API documentation, tutorials, benchmarks, and generated web pages. The package documents reusable algorithm families rather than creating separate theory pages for every thin workflow wrapper.

Coverage Matrix¶

Implementation	Theory / method note	Web page	Tutorial coverage	Benchmark / result coverage
`run_vqc(...)`, `QuantumClassifier`	`docs/qml/variational_quantum_classifier.md`	`variational-quantum-classifier.html`	`notebooks/tutorials/03-variational-quantum-classifier.ipynb`, `notebooks/tutorials/07-variational-quantum-estimators.ipynb`	classification, capacity, finite-shot, noise-model, runtime-scaling benchmarks
`run_vqr(...)`, `QuantumRegressor`	`docs/qml/variational_regression.md`	`variational-regression.html`	`notebooks/tutorials/04-variational-quantum-regressor.ipynb`, `notebooks/tutorials/07-variational-quantum-estimators.ipynb`	regression, capacity, finite-shot, noise-model, runtime-scaling benchmarks
`run_qcnn(...)`	`docs/qml/qcnn.md`	`qcnn.html`	`notebooks/tutorials/10-quantum-convolutional-neural-network.ipynb`	classification, capacity, finite-shot benchmarks
`run_quantum_autoencoder(...)`	`docs/qml/autoencoder.md`	`autoencoder.html`	`notebooks/tutorials/11-quantum-autoencoder.ipynb`	API reference results
`run_quantum_kernel_classifier(...)`, `QuantumKernelClassifier`	`docs/qml/quantum_kernels.md`	`quantum-kernels.html`	`notebooks/tutorials/05-quantum-kernel-classifier.ipynb`, `notebooks/tutorials/06-quantum-kernel-estimators.ipynb`	classification, kernel-family, finite-shot, noise-model benchmarks
`QuantumKernelRegressor`	`docs/qml/quantum_kernels.md`	`quantum-kernels.html`	`notebooks/tutorials/06-quantum-kernel-estimators.ipynb`	regression, finite-shot, noise-model benchmarks
`run_trainable_quantum_kernel_classifier(...)`	`docs/qml/advanced_kernels.md`	`advanced-kernels.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	classification and kernel-family benchmarks
`TrainableQuantumKernelRegressor`, `run_trainable_quantum_kernel_regressor(...)`	`docs/qml/advanced_kernels.md`	`advanced-kernels.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	regression benchmarks
`QuantumKernelPCA`	`docs/qml/advanced_kernels.md`	`advanced-kernels.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	real-example notebooks
`QuantumOneClassClassifier`	`docs/qml/advanced_kernels.md`	`advanced-kernels.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	real-example notebooks
`QuantumGaussianProcessRegressor`	`docs/qml/advanced_kernels.md`	`advanced-kernels.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	regression and real-example notebooks
`QuantumReservoirFeatures`, `QuantumReservoirClassifier`, `QuantumReservoirRegressor`	`docs/qml/quantum_reservoirs.md`	`quantum-reservoirs.html`	`notebooks/tutorials/12-advanced-quantum-kernel-and-reservoir-models.ipynb`	classification, regression, finite-shot, runtime-scaling, and real-example notebooks
`run_quantum_metric_learner(...)`	`docs/qml/metric_learning.md`	`metric-learning.html`	`notebooks/tutorials/09-quantum-metric-learning.ipynb`	classification benchmarks

Supporting Pages¶

Support surface	Documentation	Web page
Embeddings and ansatz helpers	`docs/qml/embeddings.md`	`embeddings.html`
Noise models	`docs/qml/noise_models.md`	`noise-models.html`
Classical baselines	`docs/qml/classical_baselines.md`	`classical-baselines.html`
Benchmark helpers	`docs/qml/benchmarks.md`	`benchmarks.html`
Model selection helpers	`docs/qml/model_selection.md`	`model-selection.html`
Estimator consistency checklist	`docs/qml/estimator_consistency.md`	`estimator-consistency.html`
Implementation contracts	`docs/qml/implementation_contracts.md`	`implementation-contracts.html`

Documentation Policy¶

Each advertised QML algorithm should have:

a theory or method note in docs/qml/
a generated web page in the static site
API reference coverage
at least one tutorial, benchmark, or real-example notebook path
implementation-contract coverage for objective, circuit family, readout, and metric semantics

Thin estimator wrappers share the theory page of the underlying algorithm. For example, QuantumClassifier and QuantumRegressor share the VQC and VQR theory pages because they expose sklearn-style fit, predict, and score methods around the same variational circuit families.