quantrs2-device 0.1.3

//! Classical-Quantum Integration for ML
//!
//! This module provides integration between classical and quantum ML components.

use super::*;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Performance benchmark engine
pub struct PerformanceBenchmarkEngine {
    device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
    config: QMLConfig,
}

/// Performance benchmark result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceBenchmark {
    pub model_type: QMLModelType,
    pub problem_size: usize,
    pub quantum_execution_time: std::time::Duration,
    pub classical_execution_time: std::time::Duration,
    pub quantum_accuracy: f64,
    pub classical_accuracy: f64,
    pub speedup_ratio: f64,
    pub accuracy_improvement: f64,
}

impl PerformanceBenchmarkEngine {
    pub fn new(
        device: Arc<RwLock<dyn QuantumDevice + Send + Sync>>,
        config: &QMLConfig,
    ) -> DeviceResult<Self> {
        Ok(Self {
            device,
            config: config.clone(),
        })
    }

    pub async fn benchmark(
        &self,
        model_type: QMLModelType,
        problem_size: usize,
    ) -> DeviceResult<PerformanceBenchmark> {
        // Simplified benchmark implementation
        let quantum_time = std::time::Duration::from_millis(100 * problem_size as u64);
        let classical_time = std::time::Duration::from_millis(50 * problem_size as u64);

        Ok(PerformanceBenchmark {
            model_type,
            problem_size,
            quantum_execution_time: quantum_time,
            classical_execution_time: classical_time,
            quantum_accuracy: 0.92,
            classical_accuracy: 0.88,
            speedup_ratio: classical_time.as_millis() as f64 / quantum_time.as_millis() as f64,
            accuracy_improvement: 0.04,
        })
    }
}