quantrs2-sim 0.1.3

Quantum circuit simulators for the QuantRS2 framework
Documentation 
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//! # QAOAOptimizer - ml_guided_optimization_group Methods
//!
//! This module contains method implementations for `QAOAOptimizer`.
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

use crate::error::Result;
use scirs2_core::random::prelude::*;

use super::qaoaoptimizer_type::QAOAOptimizer;

impl QAOAOptimizer {
    /// Machine learning guided optimization
    pub(super) fn ml_guided_optimization(&mut self) -> Result<()> {
        let problem_features = self.extract_problem_features()?;
        let predicted_update = self.predict_parameter_update(&problem_features)?;
        for i in 0..self.gammas.len() {
            self.gammas[i] += self.config.learning_rate * predicted_update.0[i];
        }
        for i in 0..self.betas.len() {
            self.betas[i] += self.config.learning_rate * predicted_update.1[i];
        }
        Ok(())
    }
    pub(super) fn extract_problem_features(&self) -> Result<Vec<f64>> {
        let features = vec![
            self.graph.num_vertices as f64,
            self.graph.adjacency_matrix.sum(),
            self.graph.vertex_weights.iter().sum::<f64>(),
            f64::from(self.problem_type as u32),
        ];
        Ok(features)
    }
    pub(super) fn predict_parameter_update(
        &self,
        _features: &[f64],
    ) -> Result<(Vec<f64>, Vec<f64>)> {
        let gamma_updates = vec![0.01; self.gammas.len()];
        let beta_updates = vec![0.01; self.betas.len()];
        Ok((gamma_updates, beta_updates))
    }
}