Architecture#
Module Layout#
psipose/
├── core/ # Core protocols
│ ├── ansatz.py # Ansatz protocol
│ ├── feature_map.py # FeatureMap protocol
│ ├── measurement.py # Measurement protocol
│ └── quantum_model.py # QuantumModel protocol
├── feature_maps/ # Data encoding circuits
│ ├── base.py # FeatureMap abstract class
│ ├── angle.py # AngleFeatureMap
│ └── amplitude.py # AmplitudeFeatureMap
├── ansatze/ # Parameterized quantum circuits
│ ├── base.py # Ansatz abstract class
│ ├── hardware_efficient.py # HardwareEfficientAnsatz
│ └── strongly_entangling.py # StronglyEntanglingAnsatz
├── measurements/ # Quantum measurements
│ ├── base.py # Measurement abstract class
│ ├── expectation.py # PauliZExpectation, etc.
│ └── probability.py # ProbabilityMeasurement
├── models/ # Pure quantum models
│ ├── variational.py # VariationalModel
│ └── kernel.py # QuantumKernel
├── estimators/ # sklearn-compatible estimators
│ ├── base.py # QuantumEstimator
│ ├── classification.py # VQCClassifier, QSVC
│ └── regression.py # VQCRegressor
├── training/ # Training utilities
│ ├── loss.py # Loss functions
│ └── optimizer.py # Optimizer adapter
└── preprocessing/ # Sklearn transformers
├── angle_scaler.py
└── amplitude_normalizer.py
Key Abstractions#
FeatureMap: Takes classical features and applies them to qubits.
Ansatz: A parametrized quantum circuit with trainable weights.
Measurement: Quantum measurement operator.
Estimator: The sklearn-compatible class combining feature map + ansatz + measurement + optimizer.
VariationalModel: Pure quantum model combining feature map + ansatz + measurement.
QuantumKernel: Computes kernel matrix from quantum state overlaps.