use super::config::EnhancedHybridConfig;
use super::history::OptimizationHistory;
use super::results::{BenchmarkMeasurement, BenchmarkReport};
use quantrs2_core::QuantRS2Result;
use scirs2_core::ndarray::Array1;
use std::collections::HashMap;
use std::time::{Duration, Instant};
pub(crate) struct PerformanceTuner;
impl PerformanceTuner {
pub fn new() -> Self {
Self
}
pub fn tune(
&self,
_config: &mut EnhancedHybridConfig,
_history: &OptimizationHistory,
) -> QuantRS2Result<()> {
Ok(())
}
}
pub(crate) struct HybridBenchmarker {
measurements: Vec<BenchmarkMeasurement>,
}
impl HybridBenchmarker {
pub fn new() -> Self {
Self {
measurements: Vec::new(),
}
}
pub fn record_iteration(
&mut self,
history: &OptimizationHistory,
iteration: usize,
) -> QuantRS2Result<()> {
self.measurements.push(BenchmarkMeasurement {
iteration,
wall_time: Instant::now(),
cost: history.iterations.last().map(|i| i.cost).unwrap_or(0.0),
});
Ok(())
}
pub fn generate_report(&self) -> QuantRS2Result<BenchmarkReport> {
Ok(BenchmarkReport {
total_iterations: self.measurements.len(),
average_iteration_time: Duration::from_secs(1),
convergence_profile: Vec::new(),
})
}
}
pub(crate) struct DistributedExecutor;
impl DistributedExecutor {
pub fn new() -> Self {
Self
}
pub fn evaluate_distributed<F>(
&self,
cost_function: &F,
params: &Array1<f64>,
) -> QuantRS2Result<f64>
where
F: Fn(&Array1<f64>) -> QuantRS2Result<f64> + Send + Sync,
{
cost_function(params)
}
}
pub(crate) struct LocalExecutor;
impl LocalExecutor {
pub fn new() -> Self {
Self
}
}
pub(crate) struct HybridCache {
cache: HashMap<u64, CachedResult>,
max_size: usize,
}
impl HybridCache {
pub fn new() -> Self {
Self {
cache: HashMap::new(),
max_size: 10000,
}
}
}
#[derive(Clone)]
struct CachedResult {
cost: f64,
gradient: Option<Array1<f64>>,
}