Expand description
PyTorch-like API for quantum machine learning models
This module provides a familiar PyTorch-style interface for building, training, and deploying quantum ML models, making it easier for classical ML practitioners to adopt quantum algorithms.
Modules§
- quantum_
nn - Utility functions for building quantum models
Structs§
- Memory
Data Loader - Simple in-memory data loader
- Parameter
- Quantum parameter wrapper
- Quantum
Activation - Quantum activation functions
- Quantum
Conv2d - Quantum convolutional layer
- Quantum
Cross Entropy Loss - Cross Entropy loss
- Quantum
Linear - Quantum linear layer
- QuantumMSE
Loss - Mean Squared Error loss
- Quantum
Sequential - Sequential container for quantum modules
- Quantum
Trainer - Training utilities
- Training
History - Training history
Enums§
- Activation
Type - Activation function types
- Init
Type - Parameter initialization types
Traits§
- Data
Loader - Data loader trait
- Quantum
Loss - Loss functions for quantum ML
- Quantum
Module - Base trait for all quantum ML modules