quantrs2-sim 0.1.3

Quantum circuit simulators for the QuantRS2 framework
Documentation
//! Metal-accelerated linear algebra operations for macOS
//!
//! This module provides GPU-accelerated linear algebra using Metal Performance Shaders (MPS)
//! and Accelerate framework integration.
//!
//! TODO: Implement using:
//! - Metal Performance Shaders (MPS) for matrix operations
//! - Accelerate.framework for optimized BLAS/LAPACK on Apple Silicon
//! - Custom Metal compute shaders for quantum-specific operations

use crate::error::{Result, SimulatorError};
use scirs2_core::ndarray::{Array2, ArrayView2};
use scirs2_core::Complex64;
use std::sync::Arc;

/// Metal-accelerated linear algebra backend for macOS
pub struct MetalLinalgBackend {
    /// Metal device handle (placeholder)
    _device: Arc<()>,
    /// Enable performance profiling
    pub enable_profiling: bool,
}

impl MetalLinalgBackend {
    /// Create a new Metal linear algebra backend
    pub fn new() -> Result<Self> {
        // TODO: Initialize Metal device
        // TODO: Create MPS context
        // TODO: Setup Accelerate framework integration

        Err(SimulatorError::GpuError(
            "Metal linear algebra not yet implemented. Please use CPU linear algebra on macOS."
                .to_string(),
        ))
    }

    /// Create an instance optimized for quantum machine learning
    pub fn new_qml_optimized() -> Result<Self> {
        // TODO: Configure for QML workloads
        // - Optimize for small-to-medium matrix operations
        // - Enable tensor operations
        // - Configure for gradient computations

        Err(SimulatorError::GpuError(
            "Metal QML optimization not yet implemented".to_string(),
        ))
    }

    /// Matrix multiplication using Metal Performance Shaders
    pub fn matmul(
        &self,
        _a: &ArrayView2<Complex64>,
        _b: &ArrayView2<Complex64>,
    ) -> Result<Array2<Complex64>> {
        // TODO: Use MPSMatrixMultiplication
        Err(SimulatorError::GpuError(
            "Metal matrix multiplication not yet implemented".to_string(),
        ))
    }

    /// Eigenvalue decomposition using Accelerate framework
    pub fn eig(
        &self,
        _matrix: &ArrayView2<Complex64>,
    ) -> Result<(Array2<Complex64>, Array2<Complex64>)> {
        // TODO: Use Accelerate's LAPACK routines
        Err(SimulatorError::GpuError(
            "Metal eigenvalue decomposition not yet implemented".to_string(),
        ))
    }

    /// Singular value decomposition
    pub fn svd(
        &self,
        _matrix: &ArrayView2<Complex64>,
    ) -> Result<(Array2<Complex64>, Array2<f64>, Array2<Complex64>)> {
        // TODO: Use Accelerate's LAPACK routines or MPS
        Err(SimulatorError::GpuError(
            "Metal SVD not yet implemented".to_string(),
        ))
    }

    /// Check if Metal Performance Shaders is available
    pub const fn is_mps_available() -> bool {
        // TODO: Check for MPS availability
        // Requires macOS 10.13+ for basic MPS
        // Requires macOS 11.0+ for advanced features
        // Best performance on Apple Silicon
        false
    }

    /// Get Metal device capabilities
    pub fn get_device_info() -> String {
        // TODO: Query Metal device capabilities
        // - GPU family (Apple, Intel, AMD)
        // - Unified memory availability
        // - Maximum buffer size
        // - Compute units
        "Metal device info not yet available".to_string()
    }
}

// Future implementation notes:
//
// 1. Metal Shaders for Quantum Gates:
//    - Implement custom compute shaders for Pauli gates
//    - Optimize for sparse operations
//    - Use threadgroup memory for local computations
//
// 2. Memory Management:
//    - Leverage unified memory on Apple Silicon
//    - Implement efficient buffer management
//    - Use shared memory between CPU and GPU
//
// 3. Performance Optimizations:
//    - Tile-based rendering for large state vectors
//    - Parallel command encoding
//    - Async compute with multiple command queues
//
// 4. Integration with Accelerate:
//    - Use vDSP for signal processing
//    - Use BLAS for basic operations
//    - Use LAPACK for advanced decompositions