quantrs2_device/quantum_ml/
hardware_acceleration.rs

1//! Hardware Acceleration for Quantum ML
2//!
3//! This module provides hardware acceleration capabilities for quantum ML training and inference.
4
5use super::*;
6use serde::{Deserialize, Serialize};
7use std::collections::HashMap;
8
9/// Hardware acceleration manager
10pub struct HardwareAccelerationManager {
11    config: QMLConfig,
12    acceleration_metrics: HardwareAccelerationMetrics,
13}
14
15/// Hardware acceleration metrics
16#[derive(Debug, Clone, Serialize, Deserialize)]
17pub struct HardwareAccelerationMetrics {
18    pub quantum_advantage_ratio: f64,
19    pub classical_speedup: f64,
20    pub quantum_circuit_time: std::time::Duration,
21    pub classical_equivalent_time: std::time::Duration,
22    pub hardware_utilization: f64,
23}
24
25impl HardwareAccelerationManager {
26    pub fn new(config: &QMLConfig) -> DeviceResult<Self> {
27        Ok(Self {
28            config: config.clone(),
29            acceleration_metrics: HardwareAccelerationMetrics {
30                quantum_advantage_ratio: 1.0,
31                classical_speedup: 1.0,
32                quantum_circuit_time: std::time::Duration::from_millis(100),
33                classical_equivalent_time: std::time::Duration::from_millis(100),
34                hardware_utilization: 0.8,
35            },
36        })
37    }
38
39    pub async fn initialize(&mut self) -> DeviceResult<()> {
40        // Initialize hardware acceleration
41        Ok(())
42    }
43
44    pub async fn shutdown(&mut self) -> DeviceResult<()> {
45        // Shutdown hardware acceleration
46        Ok(())
47    }
48
49    pub async fn get_metrics(&self) -> HardwareAccelerationMetrics {
50        self.acceleration_metrics.clone()
51    }
52
53    pub async fn get_quantum_advantage_ratio(&self) -> f64 {
54        self.acceleration_metrics.quantum_advantage_ratio
55    }
56}
quantrs2_device/quantum_ml/hardware_acceleration.rs

quantrs2_device/quantum_ml/
hardware_acceleration.rs
