How mehdibp/ariaquanta works
AriaQuanta is a Python quantum-computing library spanning quantum algorithms, circuit construction and simulation, and a variational quantum machine-learning toolkit. Its QML capabilities cover data encodings, kernel feature maps, trainable ansätze, gradient estimation, losses and optimizers, VQC models with parameter persistence, and barren-plateau diagnostics. The excerpts show selected circuit construction and ansatz/gradient behavior, plus VQC parameter loading; most algorithm, backend, and training interactions are unsampled, so their connections are left out rather than inferred.
Main entry points: ansatz Variational ansätze · encoding Data encodings · data_reuploading.py Data re-uploading · hardware_efficient.py Hardware-efficient
Components 24
Quantum algorithms
- Algorithm suite
algorithms - Eigen solver
algorithms/eigen_solver.py
Circuits and simulation
- Circuit engine
aqc/circuit.py - Circuit ansatz
aqc/ansatz.py - Gate library
aqc/gatelibrary - Hamiltonians
aqc/hamiltonian.py - Measurement and noise
aqc - Simulator backend
backend/simulator.py - Jobs and results
backend
Quantum machine learning
- Data encodings
qml/encoding - Kernel feature maps
qml/feature_map - Pauli map layers
qml/feature_map/unit.py - Variational ansätze
qml/ansatz - Data re-uploading
qml/ansatz/data_reuploading.py - Hardware-efficient
qml/ansatz/hardware_efficient.py
QML training
- VQC model
qml/models/vqc.py - Parameter persistence
qml/models/persistence.py - Gradient estimators
qml/gradients - Parameter shift
qml/gradients/parameter_shift.py - SPSA gradient
qml/gradients/spsa.py - Loss functions
qml/training/loss.py - Optimizers
qml/training/optimizer.py - Plateau diagnostics
qml/diagnostics/barren_plateau.py
Other components
- Library user
Connections 12
- Library user → Algorithm suite: selects algorithms
- Library user → Data encodings: encodes data
- Data encodings → Circuit engine: builds circuits
qml/encoding/qram.py - Kernel feature maps → Pauli map layers: applies layers
qml/feature_map/pauli.py - Variational ansätze → Data re-uploading: provides template
qml/ansatz/__init__.py - Variational ansätze → Hardware-efficient: provides template
qml/ansatz/__init__.py - Data re-uploading → Gate library: adds gates
qml/ansatz/data_reuploading.py - Hardware-efficient → Gate library: adds gates
qml/ansatz/hardware_efficient.py - Parameter shift → Circuit ansatz: perturbs parameters
qml/gradients/parameter_shift.py - SPSA gradient → Circuit ansatz: perturbs parameters
qml/gradients/spsa.py - Plateau diagnostics → Circuit engine: runs circuit
qml/diagnostics/barren_plateau.py - VQC model → Parameter persistence: loads parameters
qml/models/vqc.py