Expand description
Enhanced Automatic Differentiation for Quantum Gradients
This module provides advanced automatic differentiation capabilities specifically designed for quantum computing, including parameter-shift rules, finite differences, and hybrid classical-quantum gradients.
Structs§
- Cache
Entry - Computation
Edge - Computation
Graph - Computation graph for tracking quantum operations
- Computation
Node - Gradient
Cache - Cache for computed gradients
- Gradient
Result - Result of gradient computation
- Higher
Order Result - Higher-order derivative result
- Parameter
- Parameter
Registry - Registry for tracking parameters
- Quantum
Auto Diff - Quantum automatic differentiation engine
- Quantum
Auto Diff Config - Configuration for quantum automatic differentiation
- Quantum
Auto Diff Factory - Factory for creating quantum autodiff engines with different configurations
Enums§
- Differentiation
Method - Methods for computing quantum gradients
- Optimizer
Type - Quantum
Operation