quantrs2-ml 0.1.3

Quantum Machine Learning module for QuantRS2
Documentation 
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//! Optimizer implementations for hybrid algorithms

use super::config::OptimizerType;
use super::history::OptimizationHistory;
use quantrs2_core::QuantRS2Result;
use scirs2_core::ndarray::Array1;
use std::collections::HashMap;

/// Hybrid optimizer
pub(crate) struct HybridOptimizer {
    optimizer_type: OptimizerType,
    pub state: OptimizerState,
}

impl HybridOptimizer {
    pub fn new(optimizer_type: OptimizerType) -> Self {
        Self {
            optimizer_type,
            state: OptimizerState::new(),
        }
    }

    pub fn update_parameters(
        &mut self,
        params: &Array1<f64>,
        gradient: &Array1<f64>,
    ) -> QuantRS2Result<Array1<f64>> {
        match self.optimizer_type {
            OptimizerType::Adam => self.adam_update(params, gradient),
            OptimizerType::GradientDescent => self.gd_update(params, gradient),
            _ => Ok(params.clone()),
        }
    }

    fn adam_update(
        &mut self,
        params: &Array1<f64>,
        gradient: &Array1<f64>,
    ) -> QuantRS2Result<Array1<f64>> {
        // Adam optimizer implementation
        let beta1 = 0.9;
        let beta2 = 0.999;
        let epsilon = 1e-8;

        self.state.iteration += 1;

        // Update biased first moment estimate
        self.state.m = beta1 * &self.state.m + (1.0 - beta1) * gradient;

        // Update biased second raw moment estimate
        self.state.v = beta2 * &self.state.v + (1.0 - beta2) * gradient.mapv(|x| x.powi(2));

        // Compute bias-corrected first moment estimate
        let m_hat = &self.state.m / (1.0 - beta1.powi(self.state.iteration as i32));

        // Compute bias-corrected second raw moment estimate
        let v_hat = &self.state.v / (1.0 - beta2.powi(self.state.iteration as i32));

        // Update parameters
        Ok(params - self.state.learning_rate * m_hat / (v_hat.mapv(f64::sqrt) + epsilon))
    }

    fn gd_update(
        &self,
        params: &Array1<f64>,
        gradient: &Array1<f64>,
    ) -> QuantRS2Result<Array1<f64>> {
        Ok(params - self.state.learning_rate * gradient)
    }
}

#[derive(Clone)]
pub(crate) struct OptimizerState {
    pub iteration: usize,
    pub learning_rate: f64,
    pub m: Array1<f64>, // First moment vector
    pub v: Array1<f64>, // Second moment vector
}

impl OptimizerState {
    pub fn new() -> Self {
        Self {
            iteration: 0,
            learning_rate: 0.001,
            m: Array1::zeros(1),
            v: Array1::zeros(1),
        }
    }
}

/// ML-enhanced optimizer
pub(crate) struct MLHybridOptimizer {
    models: HashMap<String, Box<dyn OptimizationModel>>,
}

impl MLHybridOptimizer {
    pub fn new() -> Self {
        Self {
            models: HashMap::new(),
        }
    }

    pub fn enhance_gradient(
        &self,
        gradient: &Array1<f64>,
        _history: &OptimizationHistory,
    ) -> QuantRS2Result<Array1<f64>> {
        // ML enhancement of gradient
        Ok(gradient.clone())
    }
}

/// Optimization model trait
pub(crate) trait OptimizationModel: Send + Sync {
    fn optimize(&self, history: &OptimizationHistory) -> Array1<f64>;
    fn predict_convergence(&self, history: &OptimizationHistory) -> usize;
}