quantrs2-device 0.1.3

//! Hardware-aware optimization and calibration for VQA
//!
//! This module provides hardware-specific optimizations and calibration
//! tools for variational quantum algorithms.

use crate::DeviceResult;
use std::collections::HashMap;

/// Hardware-aware VQA configuration
#[derive(Debug, Clone)]
pub struct HardwareConfig {
    /// Device-specific parameters
    pub device_params: HashMap<String, f64>,
    /// Noise mitigation enabled
    pub noise_mitigation: bool,
    /// Hardware constraints
    pub constraints: HardwareConstraints,
}

/// Hardware constraints for VQA execution
#[derive(Debug, Clone)]
pub struct HardwareConstraints {
    /// Maximum circuit depth
    pub max_depth: usize,
    /// Maximum number of qubits
    pub max_qubits: usize,
    /// Supported gate set
    pub gate_set: Vec<String>,
}

impl Default for HardwareConfig {
    fn default() -> Self {
        Self {
            device_params: HashMap::new(),
            noise_mitigation: true,
            constraints: HardwareConstraints::default(),
        }
    }
}

impl Default for HardwareConstraints {
    fn default() -> Self {
        Self {
            max_depth: 100,
            max_qubits: 50,
            gate_set: vec!["H".to_string(), "CNOT".to_string(), "RZ".to_string()],
        }
    }
}

/// Hardware optimization results
#[derive(Debug, Clone)]
pub struct HardwareOptimizationResult {
    /// Optimized parameters
    pub parameters: Vec<f64>,
    /// Hardware efficiency score
    pub efficiency: f64,
    /// Estimated fidelity
    pub fidelity: f64,
}

/// Perform hardware-aware parameter optimization
pub fn optimize_for_hardware(
    initial_params: &[f64],
    config: &HardwareConfig,
) -> DeviceResult<HardwareOptimizationResult> {
    // Basic hardware optimization implementation
    Ok(HardwareOptimizationResult {
        parameters: initial_params.to_vec(),
        efficiency: 0.8,
        fidelity: 0.95,
    })
}