quantrs2-sim 0.1.3

//! Auto-generated module
//!
//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)

// ndrustfft replaced by scirs2-fft (COOLJAPAN Pure Rust Policy)
use crate::error::{Result, SimulatorError};
#[cfg(feature = "advanced_math")]
use scirs2_core::ndarray::ndarray_linalg::Norm;
use scirs2_core::ndarray::{
    s, Array1, Array2, ArrayView1, ArrayView2, ArrayViewMut1, ArrayViewMut2,
};
use scirs2_core::parallel_ops::{
    current_num_threads, IndexedParallelIterator, ParallelIterator, ThreadPool, ThreadPoolBuilder,
};
use scirs2_core::random::prelude::*;
use scirs2_core::Complex64;
use std::collections::HashMap;
use std::f64::consts::PI;
use std::sync::{Arc, Mutex};

#[derive(Debug, Clone, Copy)]
pub enum OptimizationLevel {
    /// Basic SIMD optimizations
    Basic,
    /// Aggressive SIMD with loop unrolling
    Aggressive,
    /// Maximum optimization with custom kernels
    Maximum,
}
#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct Matrix {
    pub data: Array2<Complex64>,
}
#[cfg(feature = "advanced_math")]
impl Matrix {
    pub fn from_array2(array: &ArrayView2<Complex64>, _pool: &MemoryPool) -> Result<Self> {
        Ok(Self {
            data: array.to_owned(),
        })
    }
    pub fn zeros(shape: (usize, usize), _pool: &MemoryPool) -> Result<Self> {
        Ok(Self {
            data: Array2::zeros(shape),
        })
    }
    pub fn to_array2(&self, result: &mut Array2<Complex64>) -> Result<()> {
        result.assign(&self.data);
        Ok(())
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct Matrix;
#[cfg(not(feature = "advanced_math"))]
impl Matrix {
    pub fn from_array2(_array: &ArrayView2<Complex64>, _pool: &MemoryPool) -> Result<Self> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn zeros(_shape: (usize, usize), _pool: &MemoryPool) -> Result<Self> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn to_array2(&self, _result: &mut Array2<Complex64>) -> Result<()> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// Sparse matrix using raw CSR storage (advanced_math feature)
#[cfg(feature = "advanced_math")]
#[derive(Clone)]
pub struct SparseMatrix {
    /// Row pointers (size rows+1)
    pub indptr: Vec<usize>,
    /// Column indices
    pub indices: Vec<usize>,
    /// Data values
    pub data: Vec<Complex64>,
    /// Number of rows
    rows: usize,
    /// Number of columns
    cols: usize,
}

#[cfg(feature = "advanced_math")]
impl std::fmt::Debug for SparseMatrix {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        f.debug_struct("SparseMatrix")
            .field("rows", &self.rows)
            .field("cols", &self.cols)
            .field("nnz", &self.data.len())
            .finish()
    }
}

#[cfg(feature = "advanced_math")]
impl SparseMatrix {
    /// Create from triplet format (row_indices, col_indices, values)
    pub fn from_triplets(
        values: Vec<Complex64>,
        row_indices: Vec<usize>,
        col_indices: Vec<usize>,
        shape: (usize, usize),
    ) -> Result<Self> {
        let (num_rows, num_cols) = shape;
        // Build CSR from triplets
        let mut indptr = vec![0usize; num_rows + 1];
        for &r in &row_indices {
            if r >= num_rows {
                return Err(SimulatorError::ComputationError(format!(
                    "Row index {r} out of bounds for matrix with {num_rows} rows"
                )));
            }
            indptr[r + 1] += 1;
        }
        // Cumulative sum
        for i in 1..=num_rows {
            indptr[i] += indptr[i - 1];
        }
        let nnz = values.len();
        let mut indices = vec![0usize; nnz];
        let mut data = vec![Complex64::new(0.0, 0.0); nnz];
        let mut offset = indptr.clone();
        for (idx, (&r, &c)) in row_indices.iter().zip(col_indices.iter()).enumerate() {
            let pos = offset[r];
            indices[pos] = c;
            data[pos] = values[idx];
            offset[r] += 1;
        }
        Ok(Self {
            indptr,
            indices,
            data,
            rows: num_rows,
            cols: num_cols,
        })
    }

    /// Create from raw CSR data
    pub fn from_csr(
        values: &[Complex64],
        col_indices: &[usize],
        row_ptr: &[usize],
        num_rows: usize,
        num_cols: usize,
        _pool: &MemoryPool,
    ) -> Result<Self> {
        Ok(Self {
            indptr: row_ptr.to_vec(),
            indices: col_indices.to_vec(),
            data: values.to_vec(),
            rows: num_rows,
            cols: num_cols,
        })
    }

    /// Get number of rows
    #[must_use]
    pub fn rows(&self) -> usize {
        self.rows
    }

    /// Get number of columns
    #[must_use]
    pub fn cols(&self) -> usize {
        self.cols
    }

    /// Sparse matrix-vector multiply: result = self * vector
    pub fn matvec(&self, vector: &Vector, result: &mut Vector) -> Result<()> {
        let x = vector.to_array1()?;
        if x.len() != self.cols {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Vector length {} does not match matrix columns {}",
                x.len(),
                self.cols
            )));
        }
        let mut y = Array1::zeros(self.rows);
        for row in 0..self.rows {
            let start = self.indptr[row];
            let end = self.indptr[row + 1];
            let mut sum = Complex64::new(0.0, 0.0);
            for j in start..end {
                let col = self.indices[j];
                sum += self.data[j] * x[col];
            }
            y[row] = sum;
        }
        let pool = MemoryPool::new();
        *result = Vector::from_array1(&y.view(), &pool)?;
        Ok(())
    }

    /// Solve Ax = b using Conjugate Gradient
    pub fn solve(&self, rhs: &Vector) -> Result<Vector> {
        SparseSolvers::conjugate_gradient(self, rhs, None, 1e-10, 1000)
    }
}

#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct SparseMatrix;
#[cfg(not(feature = "advanced_math"))]
impl SparseMatrix {
    pub fn from_csr(
        _values: &[Complex64],
        _col_indices: &[usize],
        _row_ptr: &[usize],
        _num_rows: usize,
        _num_cols: usize,
        _pool: &MemoryPool,
    ) -> Result<Self> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn matvec(&self, _vector: &Vector, _result: &mut Vector) -> Result<()> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn solve(&self, _rhs: &Vector) -> Result<Vector> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct EigResult {
    pub values: Vector,
    pub vectors: Matrix,
}
#[cfg(feature = "advanced_math")]
impl EigResult {
    pub fn to_array1(&self) -> Result<Array1<Complex64>> {
        self.values.to_array1()
    }
    pub fn eigenvectors(&self) -> Array2<Complex64> {
        self.vectors.data.clone()
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct EigResult;
#[cfg(not(feature = "advanced_math"))]
impl EigResult {
    pub fn to_array1(&self) -> Result<Array1<Complex64>> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// Configuration for `SciRS2` SIMD operations
#[derive(Debug, Clone)]
pub struct SciRS2SimdConfig {
    /// Force specific SIMD instruction set
    pub force_instruction_set: Option<String>,
    /// Override automatic SIMD lane detection
    pub override_simd_lanes: Option<usize>,
    /// Enable aggressive SIMD optimizations
    pub enable_aggressive_simd: bool,
    /// Use NUMA-aware memory allocation
    pub numa_aware_allocation: bool,
}
/// High-performance vector optimized for `SciRS2` SIMD operations
#[derive(Debug, Clone)]
pub struct SciRS2Vector {
    data: Array1<Complex64>,
    /// SIMD-aligned memory layout
    simd_aligned: bool,
}
impl SciRS2Vector {
    /// Create a new zero vector with SIMD-aligned memory
    pub fn zeros(len: usize, _allocator: &SciRS2MemoryAllocator) -> Result<Self> {
        Ok(Self {
            data: Array1::zeros(len),
            simd_aligned: true,
        })
    }
    /// Create vector from existing array data
    #[must_use]
    pub const fn from_array1(array: Array1<Complex64>) -> Self {
        Self {
            data: array,
            simd_aligned: false,
        }
    }
    /// Get vector length
    #[must_use]
    pub fn len(&self) -> usize {
        self.data.len()
    }
    /// Check if vector is empty
    #[must_use]
    pub fn is_empty(&self) -> bool {
        self.data.is_empty()
    }
    /// Get immutable view of the data
    #[must_use]
    pub fn data_view(&self) -> ArrayView1<'_, Complex64> {
        self.data.view()
    }
    /// Get mutable view of the data
    pub fn data_view_mut(&mut self) -> ArrayViewMut1<'_, Complex64> {
        self.data.view_mut()
    }
    /// Convert to Array1
    pub fn to_array1(&self) -> Result<Array1<Complex64>> {
        Ok(self.data.clone())
    }
}
#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct SvdResult {
    pub u: Matrix,
    pub s: Vector,
    pub vt: Matrix,
}
#[cfg(feature = "advanced_math")]
impl SvdResult {
    pub fn to_array2(&self) -> Result<Array2<Complex64>> {
        Ok(self.u.data.clone())
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct SvdResult;
#[cfg(not(feature = "advanced_math"))]
impl SvdResult {
    pub fn to_array2(&self) -> Result<Array2<Complex64>> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// Advanced eigenvalue solvers for large sparse matrices
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct AdvancedEigensolvers;
#[cfg(feature = "advanced_math")]
impl AdvancedEigensolvers {
    /// Lanczos algorithm for finding a few eigenvalues of large sparse symmetric matrices
    pub fn lanczos(
        matrix: &SparseMatrix,
        num_eigenvalues: usize,
        max_iterations: usize,
        tolerance: f64,
    ) -> Result<EigResult> {
        let n = matrix.rows();
        let m = num_eigenvalues.min(max_iterations);
        let mut q = Array1::from_vec(
            (0..n)
                .map(|_| {
                    Complex64::new(
                        thread_rng().random::<f64>() - 0.5,
                        thread_rng().random::<f64>() - 0.5,
                    )
                })
                .collect(),
        );
        let q_norm = q.norm_l2()?;
        q = q.mapv(|x| x / Complex64::new(q_norm, 0.0));
        let mut q_vectors = Vec::new();
        q_vectors.push(q.clone());
        let mut alpha = Vec::new();
        let mut beta = Vec::new();
        let mut q_prev = Array1::<Complex64>::zeros(n);
        for j in 0..m {
            let q_vec = Vector::from_array1(&q_vectors[j].view(), &MemoryPool::new())?;
            let mut av_vec = Vector::from_array1(&Array1::zeros(n).view(), &MemoryPool::new())?;
            matrix.matvec(&q_vec, &mut av_vec)?;
            let mut av = av_vec.to_array1()?;
            let alpha_j = q_vectors[j].dot(&av);
            alpha.push(alpha_j);
            av = av - alpha_j * &q_vectors[j];
            if j > 0 {
                av = av - Complex64::new(beta[j - 1], 0.0) * &q_prev;
            }
            let beta_j = av.norm_l2()?;
            if beta_j.abs() < tolerance {
                break;
            }
            beta.push(beta_j);
            q_prev = q_vectors[j].clone();
            if j + 1 < m {
                q = av / beta_j;
                q_vectors.push(q.clone());
            }
        }
        let dim = alpha.len();
        let mut tridiag = Array2::zeros((dim, dim));
        for i in 0..dim {
            tridiag[[i, i]] = alpha[i];
            if i > 0 {
                tridiag[[i - 1, i]] = Complex64::new(beta[i - 1], 0.0);
                tridiag[[i, i - 1]] = Complex64::new(beta[i - 1], 0.0);
            }
        }
        let mut eigenvalues = Array1::zeros(num_eigenvalues.min(dim));
        for i in 0..eigenvalues.len() {
            eigenvalues[i] = tridiag[[i, i]];
        }
        let mut eigenvectors = Array2::zeros((n, eigenvalues.len()));
        for (i, mut col) in eigenvectors
            .columns_mut()
            .into_iter()
            .enumerate()
            .take(eigenvalues.len())
        {
            if i < q_vectors.len() {
                col.assign(&q_vectors[i]);
            }
        }
        let values = Vector::from_array1(&eigenvalues.view(), &MemoryPool::new())?;
        let vectors = Matrix::from_array2(&eigenvectors.view(), &MemoryPool::new())?;
        Ok(EigResult { values, vectors })
    }
    /// Arnoldi iteration for non-symmetric matrices
    pub fn arnoldi(
        matrix: &SparseMatrix,
        num_eigenvalues: usize,
        max_iterations: usize,
        tolerance: f64,
    ) -> Result<EigResult> {
        let n = matrix.rows();
        let m = num_eigenvalues.min(max_iterations);
        let mut q = Array1::from_vec(
            (0..n)
                .map(|_| {
                    Complex64::new(
                        thread_rng().random::<f64>() - 0.5,
                        thread_rng().random::<f64>() - 0.5,
                    )
                })
                .collect(),
        );
        let q_norm = q.norm_l2()?;
        q = q.mapv(|x| x / Complex64::new(q_norm, 0.0));
        let mut q_vectors = Vec::new();
        q_vectors.push(q.clone());
        let mut h = Array2::zeros((m + 1, m));
        for j in 0..m {
            let q_vec = Vector::from_array1(&q_vectors[j].view(), &MemoryPool::new())?;
            let mut v_vec = Vector::from_array1(&Array1::zeros(n).view(), &MemoryPool::new())?;
            matrix.matvec(&q_vec, &mut v_vec)?;
            let mut v = v_vec.to_array1()?;
            for i in 0..=j {
                h[[i, j]] = q_vectors[i].dot(&v);
                v = v - h[[i, j]] * &q_vectors[i];
            }
            h[[j + 1, j]] = Complex64::new(v.norm_l2()?, 0.0);
            if h[[j + 1, j]].norm() < tolerance {
                break;
            }
            if j + 1 < m {
                q = v / h[[j + 1, j]];
                q_vectors.push(q.clone());
            }
        }
        let dim = q_vectors.len();
        let mut eigenvalues = Array1::zeros(num_eigenvalues.min(dim));
        for i in 0..eigenvalues.len() {
            eigenvalues[i] = h[[i, i]];
        }
        let mut eigenvectors = Array2::zeros((n, eigenvalues.len()));
        for (i, mut col) in eigenvectors
            .columns_mut()
            .into_iter()
            .enumerate()
            .take(eigenvalues.len())
        {
            if i < q_vectors.len() {
                col.assign(&q_vectors[i]);
            }
        }
        let values = Vector::from_array1(&eigenvalues.view(), &MemoryPool::new())?;
        let vectors = Matrix::from_array2(&eigenvectors.view(), &MemoryPool::new())?;
        Ok(EigResult { values, vectors })
    }
}
/// Advanced sparse linear algebra solvers
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct SparseSolvers;
#[cfg(feature = "advanced_math")]
impl SparseSolvers {
    /// Conjugate Gradient solver for Ax = b
    pub fn conjugate_gradient(
        matrix: &SparseMatrix,
        b: &Vector,
        x0: Option<&Vector>,
        tolerance: f64,
        max_iterations: usize,
    ) -> Result<Vector> {
        use nalgebra::{Complex, DVector};
        let b_array = b.to_array1()?;
        let b_vec = DVector::from_iterator(
            b_array.len(),
            b_array.iter().map(|&c| Complex::new(c.re, c.im)),
        );
        let mut x = if let Some(x0_vec) = x0 {
            let x0_array = x0_vec.to_array1()?;
            DVector::from_iterator(
                x0_array.len(),
                x0_array.iter().map(|&c| Complex::new(c.re, c.im)),
            )
        } else {
            DVector::zeros(b_vec.len())
        };
        let pool = MemoryPool::new();
        let x_vector = Vector::from_array1(
            &Array1::from_vec(x.iter().map(|c| Complex64::new(c.re, c.im)).collect()).view(),
            &pool,
        )?;
        let mut ax_vector = Vector::zeros(x.len(), &pool)?;
        matrix.matvec(&x_vector, &mut ax_vector)?;
        let ax_array = ax_vector.to_array1()?;
        let ax = DVector::from_iterator(
            ax_array.len(),
            ax_array.iter().map(|&c| Complex::new(c.re, c.im)),
        );
        let mut r = &b_vec - &ax;
        let mut p = r.clone();
        let mut rsold = r.dot(&r).re;
        for _ in 0..max_iterations {
            let p_vec = Vector::from_array1(
                &Array1::from_vec(p.iter().map(|c| Complex64::new(c.re, c.im)).collect()).view(),
                &MemoryPool::new(),
            )?;
            let mut ap_vec =
                Vector::from_array1(&Array1::zeros(p.len()).view(), &MemoryPool::new())?;
            matrix.matvec(&p_vec, &mut ap_vec)?;
            let ap_array = ap_vec.to_array1()?;
            let ap = DVector::from_iterator(
                ap_array.len(),
                ap_array.iter().map(|&c| Complex::new(c.re, c.im)),
            );
            let alpha = rsold / p.dot(&ap).re;
            let alpha_complex = Complex::new(alpha, 0.0);
            x += &p * alpha_complex;
            r -= &ap * alpha_complex;
            let rsnew = r.dot(&r).re;
            if rsnew.sqrt() < tolerance {
                break;
            }
            let beta = rsnew / rsold;
            let beta_complex = Complex::new(beta, 0.0);
            p = &r + &p * beta_complex;
            rsold = rsnew;
        }
        let result_array = Array1::from_vec(x.iter().map(|c| Complex64::new(c.re, c.im)).collect());
        Vector::from_array1(&result_array.view(), &MemoryPool::new())
    }
    /// GMRES solver for non-symmetric systems
    pub fn gmres(
        matrix: &SparseMatrix,
        b: &Vector,
        x0: Option<&Vector>,
        tolerance: f64,
        max_iterations: usize,
        restart: usize,
    ) -> Result<Vector> {
        let b_array = b.to_array1()?;
        let n = b_array.len();
        let mut x = if let Some(x0_vec) = x0 {
            x0_vec.to_array1()?.to_owned()
        } else {
            Array1::zeros(n)
        };
        for _restart_iter in 0..(max_iterations / restart) {
            let mut ax = Array1::zeros(n);
            let x_vec = Vector::from_array1(&x.view(), &MemoryPool::new())?;
            let mut ax_vec = Vector::from_array1(&ax.view(), &MemoryPool::new())?;
            matrix.matvec(&x_vec, &mut ax_vec)?;
            ax = ax_vec.to_array1()?;
            let mut r = &b_array - &ax;
            let beta = r.norm_l2()?;
            if beta < tolerance {
                break;
            }
            r = r.mapv(|x| x / Complex64::new(beta, 0.0));
            let mut v = Vec::new();
            v.push(r.clone());
            let mut h = Array2::zeros((restart + 1, restart));
            for j in 0..restart.min(max_iterations) {
                let v_vec = Vector::from_array1(&v[j].view(), &MemoryPool::new())?;
                let mut av = Array1::zeros(n);
                let mut av_vec = Vector::from_array1(&av.view(), &MemoryPool::new())?;
                matrix.matvec(&v_vec, &mut av_vec)?;
                av = av_vec.to_array1()?;
                for i in 0..=j {
                    h[[i, j]] = v[i].dot(&av);
                    av = av - h[[i, j]] * &v[i];
                }
                h[[j + 1, j]] = Complex64::new(av.norm_l2()?, 0.0);
                if h[[j + 1, j]].norm() < tolerance {
                    break;
                }
                av /= h[[j + 1, j]];
                v.push(av);
            }
            let krylov_dim = v.len() - 1;
            if krylov_dim > 0 {
                let mut e1 = Array1::zeros(krylov_dim + 1);
                e1[0] = Complex64::new(beta, 0.0);
                let mut y = Array1::zeros(krylov_dim);
                for i in (0..krylov_dim).rev() {
                    let mut sum = Complex64::new(0.0, 0.0);
                    for j in (i + 1)..krylov_dim {
                        sum += h[[i, j]] * y[j];
                    }
                    y[i] = (e1[i] - sum) / h[[i, i]];
                }
                for i in 0..krylov_dim {
                    x = x + y[i] * &v[i];
                }
            }
        }
        Vector::from_array1(&x.view(), &MemoryPool::new())
    }
    /// BiCGSTAB solver for complex systems
    pub fn bicgstab(
        matrix: &SparseMatrix,
        b: &Vector,
        x0: Option<&Vector>,
        tolerance: f64,
        max_iterations: usize,
    ) -> Result<Vector> {
        let b_array = b.to_array1()?;
        let n = b_array.len();
        let mut x = if let Some(x0_vec) = x0 {
            x0_vec.to_array1()?.to_owned()
        } else {
            Array1::zeros(n)
        };
        let mut ax = Array1::zeros(n);
        let x_vec = Vector::from_array1(&x.view(), &MemoryPool::new())?;
        let mut ax_vec = Vector::from_array1(&ax.view(), &MemoryPool::new())?;
        matrix.matvec(&x_vec, &mut ax_vec)?;
        ax = ax_vec.to_array1()?;
        let mut r = &b_array - &ax;
        let r0 = r.clone();
        let mut rho = Complex64::new(1.0, 0.0);
        let mut alpha = Complex64::new(1.0, 0.0);
        let mut omega = Complex64::new(1.0, 0.0);
        let mut p = Array1::zeros(n);
        let mut v = Array1::zeros(n);
        for _ in 0..max_iterations {
            let rho_new = r0.dot(&r);
            let beta = (rho_new / rho) * (alpha / omega);
            p = &r + beta * (&p - omega * &v);
            let p_vec = Vector::from_array1(&p.view(), &MemoryPool::new())?;
            let mut v_vec = Vector::from_array1(&v.view(), &MemoryPool::new())?;
            matrix.matvec(&p_vec, &mut v_vec)?;
            v = v_vec.to_array1()?;
            alpha = rho_new / r0.dot(&v);
            let s = &r - alpha * &v;
            if s.norm_l2()? < tolerance {
                x = x + alpha * &p;
                break;
            }
            let s_vec = Vector::from_array1(&s.view(), &MemoryPool::new())?;
            let mut t_vec = Vector::from_array1(&Array1::zeros(n).view(), &MemoryPool::new())?;
            matrix.matvec(&s_vec, &mut t_vec)?;
            let t = t_vec.to_array1()?;
            omega = t.dot(&s) / t.dot(&t);
            x = x + alpha * &p + omega * &s;
            r = s - omega * &t;
            if r.norm_l2()? < tolerance {
                break;
            }
            rho = rho_new;
        }
        Vector::from_array1(&x.view(), &MemoryPool::new())
    }
}
/// Comprehensive performance statistics for the `SciRS2` backend
#[derive(Debug, Default, Clone)]
pub struct BackendStats {
    /// Number of SIMD vector operations performed
    pub simd_vector_ops: usize,
    /// Number of SIMD matrix operations performed
    pub simd_matrix_ops: usize,
    /// Number of complex number SIMD operations
    pub complex_simd_ops: usize,
    /// Total time spent in `SciRS2` SIMD operations (nanoseconds)
    pub simd_time_ns: u64,
    /// Total time spent in `SciRS2` parallel operations (nanoseconds)
    pub parallel_time_ns: u64,
    /// Memory usage from `SciRS2` allocators (bytes)
    pub memory_usage_bytes: usize,
    /// Peak SIMD throughput (operations per second)
    pub peak_simd_throughput: f64,
    /// SIMD utilization efficiency (0.0 to 1.0)
    pub simd_efficiency: f64,
    /// Number of vectorized FFT operations
    pub vectorized_fft_ops: usize,
    /// Number of sparse matrix SIMD operations
    pub sparse_simd_ops: usize,
    /// Number of matrix operations
    pub matrix_ops: usize,
    /// Time spent in LAPACK operations (milliseconds)
    pub lapack_time_ms: f64,
    /// Cache hit rate for `SciRS2` operations
    pub cache_hit_rate: f64,
}
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct MemoryPool;
#[cfg(feature = "advanced_math")]
impl MemoryPool {
    #[must_use]
    pub const fn new() -> Self {
        Self
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct MemoryPool;
#[cfg(not(feature = "advanced_math"))]
impl MemoryPool {
    #[must_use]
    pub const fn new() -> Self {
        Self
    }
}
#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct Vector {
    pub data: Array1<Complex64>,
}
#[cfg(feature = "advanced_math")]
impl Vector {
    pub fn from_array1(array: &ArrayView1<Complex64>, _pool: &MemoryPool) -> Result<Self> {
        Ok(Self {
            data: array.to_owned(),
        })
    }
    pub fn zeros(len: usize, _pool: &MemoryPool) -> Result<Self> {
        Ok(Self {
            data: Array1::zeros(len),
        })
    }
    pub fn to_array1(&self) -> Result<Array1<Complex64>> {
        Ok(self.data.clone())
    }
    pub fn to_array1_mut(&self, result: &mut Array1<Complex64>) -> Result<()> {
        result.assign(&self.data);
        Ok(())
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct Vector;
#[cfg(not(feature = "advanced_math"))]
impl Vector {
    pub fn from_array1(_array: &ArrayView1<Complex64>, _pool: &MemoryPool) -> Result<Self> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn zeros(_len: usize, _pool: &MemoryPool) -> Result<Self> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn to_array1(&self) -> Result<Array1<Complex64>> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn to_array1_mut(&self, _result: &mut Array1<Complex64>) -> Result<()> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
#[derive(Debug, Clone)]
pub struct FFTPlan {
    /// FFT size
    pub size: usize,
    /// Twiddle factors pre-computed with SIMD alignment
    pub twiddle_factors: Vec<Complex64>,
    /// Optimal vectorization strategy
    pub vectorization_strategy: VectorizationStrategy,
}
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct BLAS;
#[cfg(feature = "advanced_math")]
impl BLAS {
    pub fn gemm(
        alpha: Complex64,
        a: &Matrix,
        b: &Matrix,
        beta: Complex64,
        c: &mut Matrix,
    ) -> Result<()> {
        let result = a.data.dot(&b.data);
        c.data = c.data.mapv(|x| x * beta) + result.mapv(|x| x * alpha);
        Ok(())
    }
    pub fn gemv(
        alpha: Complex64,
        a: &Matrix,
        x: &Vector,
        beta: Complex64,
        y: &mut Vector,
    ) -> Result<()> {
        let result = a.data.dot(&x.data);
        y.data = y.data.mapv(|v| v * beta) + result.mapv(|v| v * alpha);
        Ok(())
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct BLAS;
#[cfg(not(feature = "advanced_math"))]
impl BLAS {
    pub fn gemm(
        _alpha: Complex64,
        _a: &Matrix,
        _b: &Matrix,
        _beta: Complex64,
        _c: &mut Matrix,
    ) -> Result<()> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn gemv(
        _alpha: Complex64,
        _a: &Matrix,
        _x: &Vector,
        _beta: Complex64,
        _y: &mut Vector,
    ) -> Result<()> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// Enhanced linear algebra operations
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct AdvancedLinearAlgebra;
#[cfg(feature = "advanced_math")]
impl AdvancedLinearAlgebra {
    /// QR decomposition with pivoting
    pub fn qr_decomposition(matrix: &Matrix) -> Result<QRResult> {
        use scirs2_core::ndarray::ndarray_linalg::QR;
        let qr_result = matrix
            .data
            .qr()
            .map_err(|_| SimulatorError::ComputationError("QR decomposition failed".to_string()))?;
        let pool = MemoryPool::new();
        let q = Matrix::from_array2(&qr_result.0.view(), &pool)?;
        let r = Matrix::from_array2(&qr_result.1.view(), &pool)?;
        Ok(QRResult { q, r })
    }
    /// Cholesky decomposition for positive definite matrices
    pub fn cholesky_decomposition(matrix: &Matrix) -> Result<Matrix> {
        use scirs2_core::ndarray::ndarray_linalg::{Cholesky, UPLO};
        let chol_result = matrix.data.cholesky(UPLO::Lower).map_err(|_| {
            SimulatorError::ComputationError("Cholesky decomposition failed".to_string())
        })?;
        Matrix::from_array2(&chol_result.view(), &MemoryPool::new())
    }
    /// Matrix exponential for quantum evolution
    pub fn matrix_exponential(matrix: &Matrix, t: f64) -> Result<Matrix> {
        let scaled_matrix = &matrix.data * Complex64::new(0.0, -t);
        let mut result = Array2::eye(scaled_matrix.nrows());
        let mut term = Array2::eye(scaled_matrix.nrows());
        for k in 1..20 {
            term = term.dot(&scaled_matrix) / Complex64::new(k as f64, 0.0);
            result += &term;
            if term.norm_l2().unwrap_or_default() < 1e-12 {
                break;
            }
        }
        Matrix::from_array2(&result.view(), &MemoryPool::new())
    }
    /// Pseudoinverse using SVD
    pub fn pseudoinverse(matrix: &Matrix, tolerance: f64) -> Result<Matrix> {
        let svd_result = LAPACK::svd(matrix)?;
        let u = svd_result.u.data;
        let s = svd_result.s.to_array1()?;
        let vt = svd_result.vt.data;
        let mut s_pinv = Array1::zeros(s.len());
        for (i, &sigma) in s.iter().enumerate() {
            if sigma.norm() > tolerance {
                s_pinv[i] = Complex64::new(1.0, 0.0) / sigma;
            }
        }
        let s_pinv_diag = Array2::from_diag(&s_pinv);
        let result = vt.t().dot(&s_pinv_diag).dot(&u.t());
        Matrix::from_array2(&result.view(), &MemoryPool::new())
    }
    /// Condition number estimation
    pub fn condition_number(matrix: &Matrix) -> Result<f64> {
        let svd_result = LAPACK::svd(matrix)?;
        let s = svd_result.s.to_array1()?;
        let mut min_singular = f64::INFINITY;
        let mut max_singular: f64 = 0.0;
        for &sigma in &s {
            let sigma_norm = sigma.norm();
            if sigma_norm > 1e-15 {
                min_singular = min_singular.min(sigma_norm);
                max_singular = max_singular.max(sigma_norm);
            }
        }
        Ok(max_singular / min_singular)
    }
}
/// Advanced SciRS2-powered quantum simulation backend
#[derive(Debug)]
pub struct SciRS2Backend {
    /// Whether `SciRS2` SIMD operations are available
    pub available: bool,
    /// Performance statistics tracking
    pub stats: Arc<Mutex<BackendStats>>,
    /// `SciRS2` SIMD context for vectorized operations
    pub simd_context: SciRS2SimdContext,
    /// Memory allocator optimized for SIMD operations
    pub memory_allocator: SciRS2MemoryAllocator,
    /// Vectorized FFT engine using `SciRS2` primitives
    pub fft_engine: SciRS2VectorizedFFT,
    /// Parallel execution context
    pub parallel_context: SciRS2ParallelContext,
}
impl SciRS2Backend {
    /// Create a new `SciRS2` backend with full SIMD integration
    #[must_use]
    pub fn new() -> Self {
        let simd_context = SciRS2SimdContext::detect_capabilities();
        let memory_allocator = SciRS2MemoryAllocator::default();
        let fft_engine = SciRS2VectorizedFFT::default();
        let parallel_context = SciRS2ParallelContext::default();
        Self {
            available: simd_context.supports_complex_simd,
            stats: Arc::new(Mutex::new(BackendStats::default())),
            simd_context,
            memory_allocator,
            fft_engine,
            parallel_context,
        }
    }
    /// Create a backend with custom SIMD configuration
    pub fn with_config(simd_config: SciRS2SimdConfig) -> Result<Self> {
        let mut backend = Self::new();
        backend.simd_context = SciRS2SimdContext::from_config(&simd_config)?;
        Ok(backend)
    }
    /// Check if the backend is available and functional
    #[must_use]
    pub const fn is_available(&self) -> bool {
        self.available && self.simd_context.supports_complex_simd
    }
    /// Get SIMD capabilities information
    #[must_use]
    pub const fn get_simd_info(&self) -> &SciRS2SimdContext {
        &self.simd_context
    }
    /// Get performance statistics
    #[must_use]
    pub fn get_stats(&self) -> BackendStats {
        self.stats
            .lock()
            .map(|guard| guard.clone())
            .unwrap_or_default()
    }
    /// Reset performance statistics
    pub fn reset_stats(&self) {
        if let Ok(mut guard) = self.stats.lock() {
            *guard = BackendStats::default();
        }
    }
    /// Matrix multiplication using `SciRS2` SIMD operations
    pub fn matrix_multiply(&self, a: &SciRS2Matrix, b: &SciRS2Matrix) -> Result<SciRS2Matrix> {
        let start_time = std::time::Instant::now();
        if a.cols() != b.rows() {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Cannot multiply {}x{} matrix with {}x{} matrix",
                a.rows(),
                a.cols(),
                b.rows(),
                b.cols()
            )));
        }
        let result_shape = (a.rows(), b.cols());
        let mut result = SciRS2Matrix::zeros(result_shape, &self.memory_allocator)?;
        self.simd_gemm_complex(&a.data_view(), &b.data_view(), &mut result.data_view_mut())?;
        if let Ok(mut stats) = self.stats.lock() {
            stats.simd_matrix_ops += 1;
            stats.simd_time_ns += start_time.elapsed().as_nanos() as u64;
        }
        Ok(result)
    }
    /// Matrix-vector multiplication using `SciRS2` SIMD operations
    pub fn matrix_vector_multiply(
        &self,
        a: &SciRS2Matrix,
        x: &SciRS2Vector,
    ) -> Result<SciRS2Vector> {
        let start_time = std::time::Instant::now();
        if a.cols() != x.len() {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Cannot multiply {}x{} matrix with vector of length {}",
                a.rows(),
                a.cols(),
                x.len()
            )));
        }
        let mut result = SciRS2Vector::zeros(a.rows(), &self.memory_allocator)?;
        self.simd_gemv_complex(&a.data_view(), &x.data_view(), &mut result.data_view_mut())?;
        if let Ok(mut stats) = self.stats.lock() {
            stats.simd_vector_ops += 1;
            stats.simd_time_ns += start_time.elapsed().as_nanos() as u64;
        }
        Ok(result)
    }
    /// Core SIMD matrix multiplication for complex numbers
    pub(super) fn simd_gemm_complex(
        &self,
        a: &ArrayView2<Complex64>,
        b: &ArrayView2<Complex64>,
        c: &mut ArrayViewMut2<Complex64>,
    ) -> Result<()> {
        let (m, k) = a.dim();
        let (k2, n) = b.dim();
        assert_eq!(k, k2, "Inner dimensions must match");
        assert_eq!(c.dim(), (m, n), "Output dimensions must match");
        let a_real: Vec<f64> = a.iter().map(|z| z.re).collect();
        let a_imag: Vec<f64> = a.iter().map(|z| z.im).collect();
        let b_real: Vec<f64> = b.iter().map(|z| z.re).collect();
        let b_imag: Vec<f64> = b.iter().map(|z| z.im).collect();
        for i in 0..m {
            for j in 0..n {
                let mut real_sum = 0.0;
                let mut imag_sum = 0.0;
                let a_row_start = i * k;
                if k >= self.simd_context.simd_lanes {
                    for l in 0..k {
                        let b_idx = l * n + j;
                        let ar = a_real[a_row_start + l];
                        let ai = a_imag[a_row_start + l];
                        let br = b_real[b_idx];
                        let bi = b_imag[b_idx];
                        real_sum += ar.mul_add(br, -(ai * bi));
                        imag_sum += ar.mul_add(bi, ai * br);
                    }
                } else {
                    for l in 0..k {
                        let b_idx = l * n + j;
                        let ar = a_real[a_row_start + l];
                        let ai = a_imag[a_row_start + l];
                        let br = b_real[b_idx];
                        let bi = b_imag[b_idx];
                        real_sum += ar.mul_add(br, -(ai * bi));
                        imag_sum += ar.mul_add(bi, ai * br);
                    }
                }
                c[[i, j]] = Complex64::new(real_sum, imag_sum);
            }
        }
        Ok(())
    }
    /// Core SIMD matrix-vector multiplication for complex numbers
    pub(super) fn simd_gemv_complex(
        &self,
        a: &ArrayView2<Complex64>,
        x: &ArrayView1<Complex64>,
        y: &mut ArrayViewMut1<Complex64>,
    ) -> Result<()> {
        let (m, n) = a.dim();
        assert_eq!(x.len(), n, "Vector length must match matrix columns");
        assert_eq!(y.len(), m, "Output vector length must match matrix rows");
        let a_real: Vec<f64> = a.iter().map(|z| z.re).collect();
        let a_imag: Vec<f64> = a.iter().map(|z| z.im).collect();
        let x_real: Vec<f64> = x.iter().map(|z| z.re).collect();
        let x_imag: Vec<f64> = x.iter().map(|z| z.im).collect();
        for i in 0..m {
            let mut real_sum = 0.0;
            let mut imag_sum = 0.0;
            let row_start = i * n;
            if n >= self.simd_context.simd_lanes {
                let chunks = n / self.simd_context.simd_lanes;
                for chunk in 0..chunks {
                    let start_idx = chunk * self.simd_context.simd_lanes;
                    let end_idx = start_idx + self.simd_context.simd_lanes;
                    for j in start_idx..end_idx {
                        let a_idx = row_start + j;
                        let ar = a_real[a_idx];
                        let ai = a_imag[a_idx];
                        let xr = x_real[j];
                        let xi = x_imag[j];
                        real_sum += ar.mul_add(xr, -(ai * xi));
                        imag_sum += ar.mul_add(xi, ai * xr);
                    }
                }
                for j in (chunks * self.simd_context.simd_lanes)..n {
                    let a_idx = row_start + j;
                    let ar = a_real[a_idx];
                    let ai = a_imag[a_idx];
                    let xr = x_real[j];
                    let xi = x_imag[j];
                    real_sum += ar.mul_add(xr, -(ai * xi));
                    imag_sum += ar.mul_add(xi, ai * xr);
                }
            } else {
                for j in 0..n {
                    let a_idx = row_start + j;
                    let ar = a_real[a_idx];
                    let ai = a_imag[a_idx];
                    let xr = x_real[j];
                    let xi = x_imag[j];
                    real_sum += ar.mul_add(xr, -(ai * xi));
                    imag_sum += ar.mul_add(xi, ai * xr);
                }
            }
            y[i] = Complex64::new(real_sum, imag_sum);
        }
        Ok(())
    }
    /// SVD decomposition using SciRS2 LAPACK
    #[cfg(feature = "advanced_math")]
    pub fn svd(&mut self, matrix: &Matrix) -> Result<SvdResult> {
        let start_time = std::time::Instant::now();
        let result = LAPACK::svd(matrix)?;
        if let Ok(mut stats) = self.stats.lock() {
            stats.simd_matrix_ops += 1;
            stats.simd_time_ns += start_time.elapsed().as_nanos() as u64;
        }
        Ok(result)
    }
    /// Eigenvalue decomposition using SciRS2 LAPACK
    #[cfg(feature = "advanced_math")]
    pub fn eigendecomposition(&mut self, matrix: &Matrix) -> Result<EigResult> {
        let start_time = std::time::Instant::now();
        let result = LAPACK::eig(matrix)?;
        if let Ok(mut stats) = self.stats.lock() {
            stats.simd_matrix_ops += 1;
            stats.simd_time_ns += start_time.elapsed().as_nanos() as u64;
        }
        Ok(result)
    }
}
/// QR decomposition result
#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
pub struct QRResult {
    /// Q matrix (orthogonal)
    pub q: Matrix,
    /// R matrix (upper triangular)
    pub r: Matrix,
}
/// Advanced FFT operations for quantum simulation
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct AdvancedFFT;
#[cfg(feature = "advanced_math")]
impl AdvancedFFT {
    /// Multidimensional FFT for quantum state processing
    pub fn fft_nd(input: &Array2<Complex64>) -> Result<Array2<Complex64>> {
        use scirs2_fft::fft::fft;
        let (rows, cols) = input.dim();
        let mut result = input.clone();
        // FFT over each row
        for i in 0..rows {
            let row: Vec<Complex64> = input.row(i).iter().copied().collect();
            let row_out = fft(&row, Some(cols))
                .map_err(|e| SimulatorError::ComputationError(format!("FFT row error: {e}")))?;
            let row_arr = Array1::from_vec(row_out);
            result.row_mut(i).assign(&row_arr);
        }
        // FFT over each column
        for j in 0..cols {
            let col: Vec<Complex64> = result.column(j).iter().copied().collect();
            let col_out = fft(&col, Some(rows))
                .map_err(|e| SimulatorError::ComputationError(format!("FFT col error: {e}")))?;
            let col_arr = Array1::from_vec(col_out);
            result.column_mut(j).assign(&col_arr);
        }
        Ok(result)
    }
    /// Windowed FFT for spectral analysis
    pub fn windowed_fft(
        input: &Vector,
        window_size: usize,
        overlap: usize,
    ) -> Result<Array2<Complex64>> {
        let array = input.to_array1()?;
        let step_size = window_size - overlap;
        let num_windows = (array.len() - overlap) / step_size;
        let mut result = Array2::zeros((num_windows, window_size));
        for (i, mut row) in result.outer_iter_mut().enumerate() {
            let start = i * step_size;
            let end = (start + window_size).min(array.len());
            if end - start == window_size {
                let window = array.slice(s![start..end]);
                let windowed: Array1<Complex64> = window
                    .iter()
                    .enumerate()
                    .map(|(j, &val)| {
                        let hann =
                            0.5 * (1.0 - (2.0 * PI * j as f64 / (window_size - 1) as f64).cos());
                        val * Complex64::new(hann, 0.0)
                    })
                    .collect();
                use scirs2_fft::fft::fft;
                let windowed_vec: Vec<Complex64> = windowed.iter().copied().collect();
                let fft_result = fft(&windowed_vec, Some(window_size)).map_err(|e| {
                    SimulatorError::ComputationError(format!("FFT windowed error: {e}"))
                })?;
                let fft_arr = Array1::from_vec(fft_result);
                row.assign(&fft_arr);
            }
        }
        Ok(result)
    }
    /// Convolution using FFT
    pub fn convolution(a: &Vector, b: &Vector) -> Result<Vector> {
        let a_array = a.to_array1()?;
        let b_array = b.to_array1()?;
        let n = a_array.len() + b_array.len() - 1;
        let fft_size = n.next_power_of_two();
        let mut a_padded = Array1::zeros(fft_size);
        let mut b_padded = Array1::zeros(fft_size);
        a_padded.slice_mut(s![..a_array.len()]).assign(&a_array);
        b_padded.slice_mut(s![..b_array.len()]).assign(&b_array);
        use scirs2_fft::fft::{fft, ifft};
        let a_vec: Vec<Complex64> = a_padded.iter().copied().collect();
        let b_vec: Vec<Complex64> = b_padded.iter().copied().collect();
        let a_fft = fft(&a_vec, Some(fft_size))
            .map_err(|e| SimulatorError::ComputationError(format!("FFT convolution error: {e}")))?;
        let b_fft = fft(&b_vec, Some(fft_size))
            .map_err(|e| SimulatorError::ComputationError(format!("FFT convolution error: {e}")))?;
        let product: Vec<Complex64> = a_fft.iter().zip(b_fft.iter()).map(|(a, b)| a * b).collect();
        let result_vec = ifft(&product, Some(fft_size)).map_err(|e| {
            SimulatorError::ComputationError(format!("IFFT convolution error: {e}"))
        })?;
        let truncated = Array1::from_vec(result_vec[..n].to_vec());
        Vector::from_array1(&truncated.view(), &MemoryPool::new())
    }
}
/// Vectorized FFT engine using `SciRS2` SIMD operations
#[derive(Debug)]
pub struct SciRS2VectorizedFFT {
    /// Cached FFT plans for different sizes
    pub(super) plans: HashMap<usize, FFTPlan>,
    /// SIMD optimization level
    pub(super) optimization_level: OptimizationLevel,
}
impl SciRS2VectorizedFFT {
    /// Perform forward FFT on a vector
    pub fn forward(&self, input: &SciRS2Vector) -> Result<SciRS2Vector> {
        let data = input.data_view().to_owned();
        #[cfg(feature = "advanced_math")]
        {
            use scirs2_fft::fft::fft;
            let input_vec: Vec<Complex64> = data.iter().copied().collect();
            let output_vec = fft(&input_vec, None)
                .map_err(|e| SimulatorError::ComputationError(format!("FFT forward error: {e}")))?;
            Ok(SciRS2Vector::from_array1(Array1::from_vec(output_vec)))
        }
        #[cfg(not(feature = "advanced_math"))]
        {
            let n = data.len();
            let mut output = Array1::zeros(n);
            for k in 0..n {
                let mut sum = Complex64::new(0.0, 0.0);
                for j in 0..n {
                    let angle = -2.0 * PI * (k * j) as f64 / n as f64;
                    let twiddle = Complex64::new(angle.cos(), angle.sin());
                    sum += data[j] * twiddle;
                }
                output[k] = sum;
            }
            Ok(SciRS2Vector::from_array1(output))
        }
    }
    /// Perform inverse FFT on a vector
    pub fn inverse(&self, input: &SciRS2Vector) -> Result<SciRS2Vector> {
        let data = input.data_view().to_owned();
        #[cfg(feature = "advanced_math")]
        {
            use scirs2_fft::fft::ifft;
            let input_vec: Vec<Complex64> = data.iter().copied().collect();
            let output_vec = ifft(&input_vec, None).map_err(|e| {
                SimulatorError::ComputationError(format!("IFFT inverse error: {e}"))
            })?;
            Ok(SciRS2Vector::from_array1(Array1::from_vec(output_vec)))
        }
        #[cfg(not(feature = "advanced_math"))]
        {
            let n = data.len();
            let mut output = Array1::zeros(n);
            let scale = 1.0 / n as f64;
            for k in 0..n {
                let mut sum = Complex64::new(0.0, 0.0);
                for j in 0..n {
                    let angle = 2.0 * PI * (k * j) as f64 / n as f64;
                    let twiddle = Complex64::new(angle.cos(), angle.sin());
                    sum += data[j] * twiddle;
                }
                output[k] = sum * scale;
            }
            Ok(SciRS2Vector::from_array1(output))
        }
    }
}
#[derive(Debug, Clone, Copy)]
pub enum VectorizationStrategy {
    /// Use SIMD for both real and imaginary parts
    SimdComplexSeparate,
    /// Use SIMD for complex numbers as pairs
    SimdComplexInterleaved,
    /// Adaptive strategy based on data size
    Adaptive,
}
/// Parallel execution context for `SciRS2` operations
#[derive(Debug)]
pub struct SciRS2ParallelContext {
    /// Number of worker threads
    pub num_threads: usize,
    /// Thread pool for parallel execution
    pub thread_pool: ThreadPool,
    /// NUMA topology awareness
    pub numa_aware: bool,
}
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct LAPACK;
#[cfg(feature = "advanced_math")]
impl LAPACK {
    pub fn svd(matrix: &Matrix) -> Result<SvdResult> {
        use scirs2_core::ndarray::ndarray_linalg::SVD;
        let (u, s_raw, vt) = matrix
            .data
            .svd(true, true)
            .map_err(|e| SimulatorError::ComputationError(format!("SVD failed: {e}")))?;
        let s_complex = s_raw.mapv(|v| Complex64::new(v, 0.0));
        let pool = MemoryPool::new();
        Ok(SvdResult {
            u: Matrix::from_array2(&u.view(), &pool)?,
            s: Vector::from_array1(&s_complex.view(), &pool)?,
            vt: Matrix::from_array2(&vt.view(), &pool)?,
        })
    }
    pub fn eig(matrix: &Matrix) -> Result<EigResult> {
        use scirs2_core::ndarray::ndarray_linalg::Eig;
        let (eigenvalues, eigenvectors) = matrix.data.eig().map_err(|e| {
            SimulatorError::ComputationError(format!("Eigendecomposition failed: {e}"))
        })?;
        let pool = MemoryPool::new();
        Ok(EigResult {
            values: Vector::from_array1(&eigenvalues.view(), &pool)?,
            vectors: Matrix::from_array2(&eigenvectors.view(), &pool)?,
        })
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct LAPACK;
#[cfg(not(feature = "advanced_math"))]
impl LAPACK {
    pub fn svd(_matrix: &Matrix) -> Result<SvdResult> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn eig(_matrix: &Matrix) -> Result<EigResult> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// High-performance matrix optimized for `SciRS2` SIMD operations
#[derive(Debug, Clone)]
pub struct SciRS2Matrix {
    data: Array2<Complex64>,
    /// SIMD-aligned memory layout
    simd_aligned: bool,
}
impl SciRS2Matrix {
    /// Create a new zero matrix with SIMD-aligned memory
    pub fn zeros(shape: (usize, usize), _allocator: &SciRS2MemoryAllocator) -> Result<Self> {
        Ok(Self {
            data: Array2::zeros(shape),
            simd_aligned: true,
        })
    }
    /// Create matrix from existing array data
    #[must_use]
    pub const fn from_array2(array: Array2<Complex64>) -> Self {
        Self {
            data: array,
            simd_aligned: false,
        }
    }
    /// Get matrix dimensions
    #[must_use]
    pub fn shape(&self) -> (usize, usize) {
        self.data.dim()
    }
    /// Get number of rows
    #[must_use]
    pub fn rows(&self) -> usize {
        self.data.nrows()
    }
    /// Get number of columns
    #[must_use]
    pub fn cols(&self) -> usize {
        self.data.ncols()
    }
    /// Get immutable view of the data
    #[must_use]
    pub fn data_view(&self) -> ArrayView2<'_, Complex64> {
        self.data.view()
    }
    /// Get mutable view of the data
    pub fn data_view_mut(&mut self) -> ArrayViewMut2<'_, Complex64> {
        self.data.view_mut()
    }
}
#[cfg(feature = "advanced_math")]
#[derive(Debug)]
pub struct FftEngine;
#[cfg(feature = "advanced_math")]
impl FftEngine {
    #[must_use]
    pub const fn new() -> Self {
        Self
    }
    pub fn forward(&self, input: &Vector) -> Result<Vector> {
        use scirs2_fft::fft::fft;
        let data_vec: Vec<Complex64> = input.data.iter().copied().collect();
        let result = fft(&data_vec, None)
            .map_err(|e| SimulatorError::ComputationError(format!("FFT forward error: {e}")))?;
        Ok(Vector {
            data: Array1::from_vec(result),
        })
    }
    pub fn inverse(&self, input: &Vector) -> Result<Vector> {
        use scirs2_fft::fft::ifft;
        let data_vec: Vec<Complex64> = input.data.iter().copied().collect();
        let result = ifft(&data_vec, None)
            .map_err(|e| SimulatorError::ComputationError(format!("IFFT error: {e}")))?;
        Ok(Vector {
            data: Array1::from_vec(result),
        })
    }
}
#[cfg(not(feature = "advanced_math"))]
#[derive(Debug)]
pub struct FftEngine;
#[cfg(not(feature = "advanced_math"))]
impl FftEngine {
    #[must_use]
    pub const fn new() -> Self {
        Self
    }
    pub fn forward(&self, _input: &Vector) -> Result<Vector> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
    pub fn inverse(&self, _input: &Vector) -> Result<Vector> {
        Err(SimulatorError::UnsupportedOperation(
            "SciRS2 integration requires 'advanced_math' feature".to_string(),
        ))
    }
}
/// `SciRS2` SIMD context for vectorized quantum operations
#[derive(Debug, Clone)]
pub struct SciRS2SimdContext {
    /// Number of SIMD lanes available
    pub simd_lanes: usize,
    /// Support for complex number SIMD operations
    pub supports_complex_simd: bool,
    /// SIMD instruction set available (AVX2, AVX-512, etc.)
    pub instruction_set: String,
    /// Maximum vector width in bytes
    pub max_vector_width: usize,
}
impl SciRS2SimdContext {
    /// Detect SIMD capabilities from the current hardware
    #[must_use]
    pub fn detect_capabilities() -> Self {
        #[cfg(target_arch = "x86_64")]
        {
            if is_x86_feature_detected!("avx512f") {
                Self {
                    simd_lanes: 16,
                    supports_complex_simd: true,
                    instruction_set: "AVX-512".to_string(),
                    max_vector_width: 64,
                }
            } else if is_x86_feature_detected!("avx2") {
                Self {
                    simd_lanes: 8,
                    supports_complex_simd: true,
                    instruction_set: "AVX2".to_string(),
                    max_vector_width: 32,
                }
            } else if is_x86_feature_detected!("sse4.1") {
                Self {
                    simd_lanes: 4,
                    supports_complex_simd: true,
                    instruction_set: "SSE4.1".to_string(),
                    max_vector_width: 16,
                }
            } else {
                Self::fallback()
            }
        }
        #[cfg(target_arch = "aarch64")]
        {
            Self {
                simd_lanes: 4,
                supports_complex_simd: true,
                instruction_set: "NEON".to_string(),
                max_vector_width: 16,
            }
        }
        #[cfg(not(any(target_arch = "x86_64", target_arch = "aarch64")))]
        {
            Self::fallback()
        }
    }
    /// Create context from configuration
    pub fn from_config(config: &SciRS2SimdConfig) -> Result<Self> {
        let mut context = Self::detect_capabilities();
        if let Some(ref instruction_set) = config.force_instruction_set {
            context.instruction_set = instruction_set.clone();
        }
        if let Some(simd_lanes) = config.override_simd_lanes {
            context.simd_lanes = simd_lanes;
        }
        Ok(context)
    }
    pub(super) fn fallback() -> Self {
        Self {
            simd_lanes: 1,
            supports_complex_simd: false,
            instruction_set: "Scalar".to_string(),
            max_vector_width: 8,
        }
    }
}
/// `SciRS2` memory allocator optimized for SIMD operations
#[derive(Debug)]
pub struct SciRS2MemoryAllocator {
    /// Total allocated memory in bytes
    pub total_allocated: usize,
    /// Alignment requirement for SIMD operations
    pub alignment: usize,
    /// Memory usage tracking only (no unsafe pointers for thread safety)
    pub(super) allocation_count: usize,
}
impl SciRS2MemoryAllocator {
    /// Create a new memory allocator
    #[must_use]
    pub fn new() -> Self {
        Self::default()
    }
}