Struct NaturalGradient 

pub struct NaturalGradient { /* private fields */ }

Natural gradient optimizer using Fisher information matrix.

Accounts for the geometry of the parameter space, which is particularly important for variational quantum circuits.

Update Rule

$$\theta_{t+1} = \theta_t - \alpha F^{-1} \nabla L$$

where F is the Fisher information matrix.

Implementations

impl NaturalGradient

pub fn new(learning_rate: f64) -> Self

Create a new natural gradient optimizer.

pub fn with_regularization(self, reg: f64) -> Self

Set regularization for Fisher matrix inversion.

pub fn compute_natural_gradient( &self, fisher: &Array2<f64>, gradient: &[f64], ) -> Result<Vec<f64>, OptimizerError>

Compute natural gradient given Fisher matrix and gradient.

Trait Implementations

impl Optimizer for NaturalGradient

fn step( &self, params: &mut [f64], gradients: &[f64], _iteration: usize, ) -> Result<(), OptimizerError>

Perform a single optimization step.

fn name(&self) -> &str

Get the optimizer name.

fn reset(&mut self)

Reset internal state (for optimizers with momentum).

fn current_learning_rate(&self, _iteration: usize) -> f64

Get current learning rate (may be adaptive).