Struct QuantumAutoDiff

pub struct QuantumAutoDiff { /* private fields */ }

Quantum automatic differentiation engine

Implementations

impl QuantumAutoDiff

pub fn new(config: QuantumAutoDiffConfig) -> Self

Create a new quantum automatic differentiation engine

pub fn register_parameter( &mut self, name: &str, initial_value: f64, bounds: Option<(f64, f64)>, ) -> QuantRS2Result<usize>

Register a parameter for differentiation

pub fn compute_gradients<F>( &mut self, function: F, parameter_ids: &[usize], method: Option<DifferentiationMethod>, ) -> QuantRS2Result<GradientResult>

where F: Fn(&[f64]) -> QuantRS2Result<Complex64> + Copy,

Compute gradients using the specified method

pub fn compute_higher_order_derivatives<F>( &mut self, function: F, parameter_ids: &[usize], max_order: usize, ) -> QuantRS2Result<HigherOrderResult>

where F: Fn(&[f64]) -> QuantRS2Result<Complex64> + Copy,

Compute higher-order derivatives

pub fn circuit_gradients<F>( &mut self, circuit_function: F, gate_parameters: &[(usize, String, Vec<usize>)], observable: &str, ) -> QuantRS2Result<Vec<GradientResult>>

where F: Fn(&[f64], &str) -> QuantRS2Result<Complex64> + Copy,

Compute gradients with respect to quantum circuit parameters

pub fn parameter_update( &mut self, gradients: &GradientResult, learning_rate: f64, optimizer: OptimizerType, ) -> QuantRS2Result<()>

Optimize parameter update using gradient information

Trait Implementations

impl Debug for QuantumAutoDiff

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.