quantrs2-device 0.1.3

//! Variational Quantum Algorithm (VQA) Support - Modular Implementation
//!
//! This module provides comprehensive support for variational quantum algorithms
//! leveraging SciRS2's advanced optimization, statistical analysis, and machine learning capabilities
//! for robust and efficient VQA execution on quantum hardware.
//!
//! The module is organized into focused submodules for maintainability and clarity:
//! - `config`: Configuration structures and enums for all VQA types
//! - `optimization`: Optimization algorithms and strategies
//! - `statistical`: Statistical analysis and validation tools
//! - `hardware`: Hardware-aware optimization and calibration
//! - `noise`: Noise mitigation and error correction
//! - `circuits`: Parametric circuit definitions and execution
//! - `objectives`: Objective function definitions and evaluation
//! - `executor`: Main VQA execution coordinator
//! - `analysis`: Performance analysis and validation

pub mod analysis;
pub mod circuits;
pub mod config;
pub mod executor;
pub mod hardware;
pub mod noise;
pub mod objectives;
pub mod optimization;
pub mod statistical;

// Re-export main types for backward compatibility
pub use analysis::*;
pub use circuits::*;
pub use config::{
    AdaptiveShotConfig, ConvergenceCriterion, GradientMethod, MultiStartConfig, VQAAlgorithmType,
    VQAConfig, VQAHardwareConfig, VQANoiseMitigation, VQAOptimizationConfig, VQAOptimizer,
    VQAStatisticalConfig, VQAValidationConfig,
}; // Selective re-export to avoid VQAResult conflicts
pub use executor::{VQAExecutor, VQAExecutorConfig, VQAResult}; // VQAResult from executor only
pub use hardware::*;
pub use noise::*;
pub use objectives::*;
pub use optimization::*;
pub use statistical::*;

// Convenient type aliases
pub type DeviceResult<T> = crate::DeviceResult<T>;
pub type DeviceError = crate::DeviceError;

// Feature-gated SciRS2 imports (re-exported for use in submodules)
#[cfg(feature = "scirs2")]
pub use scirs2_graph;
#[cfg(feature = "scirs2")]
pub use scirs2_linalg;
#[cfg(feature = "scirs2")]
pub use scirs2_optimize;
#[cfg(feature = "scirs2")]
pub use scirs2_stats;

// Fallback implementations when SciRS2 is not available
#[cfg(not(feature = "scirs2"))]
pub mod fallback_scirs2 {
    use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};

    pub fn mean(_data: &ArrayView1<f64>) -> Result<f64, String> {
        Ok(0.0)
    }
    pub fn std(_data: &ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
        Ok(1.0)
    }
    pub fn var(_data: &ArrayView1<f64>, _ddof: i32) -> Result<f64, String> {
        Ok(1.0)
    }
    pub fn pearsonr(
        _x: &ArrayView1<f64>,
        _y: &ArrayView1<f64>,
        _alt: &str,
    ) -> Result<(f64, f64), String> {
        Ok((0.0, 0.5))
    }
    pub fn trace(_matrix: &ArrayView2<f64>) -> Result<f64, String> {
        Ok(1.0)
    }
    pub fn inv(_matrix: &ArrayView2<f64>) -> Result<Array2<f64>, String> {
        Ok(Array2::eye(2))
    }

    pub struct OptimizeResult {
        pub x: Array1<f64>,
        pub fun: f64,
        pub success: bool,
        pub nit: usize,
        pub nfev: usize,
        pub message: String,
    }

    pub fn minimize(
        _func: fn(&Array1<f64>) -> f64,
        _x0: &Array1<f64>,
        _method: &str,
    ) -> Result<OptimizeResult, String> {
        Ok(OptimizeResult {
            x: Array1::zeros(2),
            fun: 0.0,
            success: true,
            nit: 0,
            nfev: 0,
            message: "Fallback optimization".to_string(),
        })
    }

    pub fn differential_evolution(
        _func: fn(&Array1<f64>) -> f64,
        _bounds: &[(f64, f64)],
    ) -> Result<OptimizeResult, String> {
        Ok(OptimizeResult {
            x: Array1::zeros(2),
            fun: 0.0,
            success: true,
            nit: 0,
            nfev: 0,
            message: "Fallback optimization".to_string(),
        })
    }
}

#[cfg(not(feature = "scirs2"))]
pub use fallback_scirs2::*;

/// Main VQA execution entry point
pub fn execute_vqa(
    config: VQAConfig,
    ansatz: &circuits::ParametricCircuit,
    objective_function: &dyn ObjectiveFunction,
) -> DeviceResult<VQAResult> {
    let mut executor =
        VQAExecutor::new(config, crate::calibration::CalibrationManager::new(), None);

    // Note: This would need to be updated to handle async execution
    // For now, returning a placeholder
    Err(DeviceError::NotImplemented(
        "Async execution not implemented in modular interface".to_string(),
    ))
}

/// Create VQA configuration for VQE algorithm
pub fn vqe_config() -> VQAConfig {
    VQAConfig::new(VQAAlgorithmType::VQE)
}

/// Create VQA configuration for QAOA algorithm
pub fn qaoa_config(num_layers: usize) -> VQAConfig {
    let mut config = VQAConfig::new(VQAAlgorithmType::QAOA);
    config.optimization_config.max_iterations = num_layers * 100;
    config
}