quantrs2-device 0.1.3

Quantum device connectors for the QuantRS2 framework
Documentation 
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//! Hardware Acceleration for Quantum ML
//!
//! This module provides hardware acceleration capabilities for quantum ML training and inference.

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

/// Hardware acceleration manager
pub struct HardwareAccelerationManager {
    config: QMLConfig,
    acceleration_metrics: HardwareAccelerationMetrics,
}

/// Hardware acceleration metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct HardwareAccelerationMetrics {
    pub quantum_advantage_ratio: f64,
    pub classical_speedup: f64,
    pub quantum_circuit_time: std::time::Duration,
    pub classical_equivalent_time: std::time::Duration,
    pub hardware_utilization: f64,
}

impl HardwareAccelerationManager {
    pub fn new(config: &QMLConfig) -> DeviceResult<Self> {
        Ok(Self {
            config: config.clone(),
            acceleration_metrics: HardwareAccelerationMetrics {
                quantum_advantage_ratio: 1.0,
                classical_speedup: 1.0,
                quantum_circuit_time: std::time::Duration::from_millis(100),
                classical_equivalent_time: std::time::Duration::from_millis(100),
                hardware_utilization: 0.8,
            },
        })
    }

    pub async fn initialize(&mut self) -> DeviceResult<()> {
        // Initialize hardware acceleration
        Ok(())
    }

    pub async fn shutdown(&mut self) -> DeviceResult<()> {
        // Shutdown hardware acceleration
        Ok(())
    }

    pub async fn get_metrics(&self) -> HardwareAccelerationMetrics {
        self.acceleration_metrics.clone()
    }

    pub async fn get_quantum_advantage_ratio(&self) -> f64 {
        self.acceleration_metrics.quantum_advantage_ratio
    }
}