quantrs2-core 0.1.3

//! Gate sequence compression using SciRS2 optimization
//!
//! This module provides advanced gate sequence compression techniques
//! leveraging SciRS2's optimization and linear algebra capabilities.

use crate::{
    error::{QuantRS2Error, QuantRS2Result},
    gate::GateOp,
    matrix_ops::matrices_approx_equal,
    qubit::QubitId,
};
use scirs2_core::ndarray::{Array2, ArrayView2};
use scirs2_core::Complex64;
// use scirs2_linalg::lowrank::{randomized_svd, truncated_svd};
// use scirs2_optimize::prelude::DifferentialEvolutionOptions;
// Tucker decomposition enabled for beta.3
use scirs2_linalg::tensor_contraction::tucker::tucker_decomposition;
// use scirs2_optimize::differential_evolution;
use crate::linalg_stubs::{randomized_svd, truncated_svd};
use crate::optimization_stubs::{differential_evolution, DifferentialEvolutionOptions};
use std::any::Any;
use std::collections::HashMap;

/// Configuration for gate sequence compression
#[derive(Debug, Clone)]
pub struct CompressionConfig {
    /// Maximum allowed error in gate approximation
    pub tolerance: f64,
    /// Maximum rank for low-rank approximations
    pub max_rank: Option<usize>,
    /// Whether to use randomized algorithms for speed
    pub use_randomized: bool,
    /// Maximum optimization iterations
    pub max_iterations: usize,
    /// Parallel execution threads
    pub num_threads: Option<usize>,
}

impl Default for CompressionConfig {
    fn default() -> Self {
        Self {
            tolerance: 1e-10,
            max_rank: None,
            use_randomized: true,
            max_iterations: 1000,
            num_threads: None,
        }
    }
}

/// Gate sequence compression optimizer
pub struct GateSequenceCompressor {
    config: CompressionConfig,
    /// Cache of compressed gates
    compression_cache: HashMap<u64, CompressedGate>,
}

/// Compressed representation of a gate
#[derive(Debug, Clone)]
pub enum CompressedGate {
    /// Low-rank approximation U ≈ AB†
    LowRank {
        left: Array2<Complex64>,
        right: Array2<Complex64>,
        rank: usize,
    },
    /// Tucker decomposition for multi-qubit gates
    Tucker {
        core: Array2<Complex64>,
        factors: Vec<Array2<Complex64>>,
    },
    /// Parameterized gate with optimized parameters
    Parameterized {
        gate_type: String,
        parameters: Vec<f64>,
        qubits: Vec<QubitId>,
    },
    /// Runtime-compressed storage with decompression function
    RuntimeCompressed {
        compressed_data: Vec<u8>,
        compression_type: CompressionType,
        original_size: usize,
        gate_metadata: GateMetadata,
    },
    /// Original gate (no compression possible)
    Original(Box<dyn GateOp>),
}

/// Type of compression used for runtime storage
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CompressionType {
    /// No compression
    None,
    /// Zlib compression
    Zlib,
    /// LZ4 compression (fast)
    LZ4,
    /// Huffman coding for sparse matrices
    Huffman,
    /// Custom quantum-specific compression
    QuantumOptimized,
}

/// Metadata for compressed gates
#[derive(Debug, Clone)]
pub struct GateMetadata {
    /// Gate name
    pub name: String,
    /// Number of qubits
    pub num_qubits: usize,
    /// Target qubits
    pub qubits: Vec<QubitId>,
    /// Matrix dimensions
    pub matrix_dims: (usize, usize),
    /// Sparsity ratio (0.0 = dense, 1.0 = all zeros)
    pub sparsity_ratio: f64,
    /// Whether the gate is unitary
    pub is_unitary: bool,
}

impl GateSequenceCompressor {
    /// Create a new gate sequence compressor
    pub fn new(config: CompressionConfig) -> Self {
        Self {
            config,
            compression_cache: HashMap::new(),
        }
    }

    /// Compress a single gate using various techniques
    pub fn compress_gate(&mut self, gate: &dyn GateOp) -> QuantRS2Result<CompressedGate> {
        let matrix_vec = gate.matrix()?;

        // Convert vector to 2D array
        let n = (matrix_vec.len() as f64).sqrt() as usize;
        let mut matrix = Array2::zeros((n, n));
        for j in 0..n {
            for i in 0..n {
                matrix[(i, j)] = matrix_vec[j * n + i];
            }
        }

        let matrix_view = matrix.view();
        let hash = self.compute_matrix_hash(&matrix_view);

        // Check cache
        if let Some(compressed) = self.compression_cache.get(&hash) {
            return Ok(compressed.clone());
        }

        // Try different compression strategies
        let compressed = if let Some(low_rank) = self.try_low_rank_approximation(&matrix_view)? {
            low_rank
        } else if let Some(tucker) = self.try_tucker_decomposition(&matrix_view)? {
            tucker
        } else if let Some(param) = self.try_parameterized_compression(gate)? {
            param
        } else if let Some(runtime_compressed) = self.try_runtime_compression(gate)? {
            runtime_compressed
        } else {
            CompressedGate::Original(gate.clone_gate())
        };

        // Cache the result
        self.compression_cache.insert(hash, compressed.clone());

        Ok(compressed)
    }

    /// Compress a sequence of gates
    pub fn compress_sequence(
        &mut self,
        gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<Vec<CompressedGate>> {
        // Set up parallel execution if configured (SciRS2 POLICY compliant)
        if let Some(threads) = self.config.num_threads {
            use scirs2_core::parallel_ops::ThreadPoolBuilder;
            ThreadPoolBuilder::new()
                .num_threads(threads)
                .build_global()
                .map_err(|e| QuantRS2Error::InvalidInput(e.to_string()))?;
        }

        // First, try to merge adjacent gates
        let merged = self.merge_adjacent_gates(gates)?;

        // Then compress each gate individually
        let compressed: Result<Vec<_>, _> = merged
            .iter()
            .map(|gate| self.compress_gate(gate.as_ref()))
            .collect();

        compressed
    }

    /// Try low-rank approximation using SVD
    fn try_low_rank_approximation(
        &self,
        matrix: &ArrayView2<Complex64>,
    ) -> QuantRS2Result<Option<CompressedGate>> {
        let (rows, cols) = matrix.dim();
        if rows != cols || rows < 4 {
            // Only try for larger gates
            return Ok(None);
        }

        // Convert to SciRS2 matrix format
        let real_part = Array2::from_shape_fn((rows, cols), |(i, j)| matrix[(i, j)].re);
        let imag_part = Array2::from_shape_fn((rows, cols), |(i, j)| matrix[(i, j)].im);

        // Try SVD-based compression
        let target_rank = self.config.max_rank.unwrap_or(rows / 2);

        // Apply SVD to real and imaginary parts separately
        let (u_real, s_real, vt_real) = if self.config.use_randomized {
            randomized_svd(&real_part.view(), target_rank, Some(10), Some(2), None)
                .map_err(|e| QuantRS2Error::InvalidInput(format!("SVD failed: {e}")))?
        } else {
            truncated_svd(&real_part.view(), target_rank, None)
                .map_err(|e| QuantRS2Error::InvalidInput(format!("SVD failed: {e}")))?
        };

        let (u_imag, s_imag, vt_imag) = if self.config.use_randomized {
            randomized_svd(&imag_part.view(), target_rank, Some(10), Some(2), None)
                .map_err(|e| QuantRS2Error::InvalidInput(format!("SVD failed: {e}")))?
        } else {
            truncated_svd(&imag_part.view(), target_rank, None)
                .map_err(|e| QuantRS2Error::InvalidInput(format!("SVD failed: {e}")))?
        };

        // Find effective rank based on singular values
        let effective_rank = self.find_effective_rank(&s_real, &s_imag)?;

        if effective_rank >= rows * 3 / 4 {
            // Not worth compressing
            return Ok(None);
        }

        // Reconstruct low-rank approximation
        let left = self.combine_complex(&u_real, &u_imag, effective_rank)?;
        let right = self.combine_complex_with_singular(
            &vt_real,
            &vt_imag,
            &s_real,
            &s_imag,
            effective_rank,
        )?;

        // Verify approximation quality
        let approx = left.dot(&right.t());
        if !matrices_approx_equal(&approx.view(), matrix, self.config.tolerance) {
            return Ok(None);
        }

        Ok(Some(CompressedGate::LowRank {
            left,
            right,
            rank: effective_rank,
        }))
    }

    /// Try Tucker decomposition for multi-qubit gates
    ///
    /// Note: Current implementation applies Tucker separately to real and imaginary parts
    /// since scirs2-linalg's Tucker only supports Float types (not Complex).
    /// TODO: Request Complex support in scirs2-linalg Tucker decomposition for better performance
    fn try_tucker_decomposition(
        &self,
        matrix: &ArrayView2<Complex64>,
    ) -> QuantRS2Result<Option<CompressedGate>> {
        let (rows, cols) = matrix.dim();

        // Only apply Tucker to larger multi-qubit gates (4x4 or larger)
        if rows < 4 || cols < 4 || rows != cols {
            return Ok(None);
        }

        // Determine target ranks for compression
        let target_rank = self.config.max_rank.unwrap_or(rows / 2).min(rows - 1);
        let ranks = vec![target_rank, target_rank];

        // Separate real and imaginary parts (scirs2-linalg Tucker only supports Float, not Complex)
        let real_part = Array2::from_shape_fn((rows, cols), |(i, j)| matrix[(i, j)].re);
        let imag_part = Array2::from_shape_fn((rows, cols), |(i, j)| matrix[(i, j)].im);

        // Apply Tucker decomposition to real part
        let tucker_real = tucker_decomposition(&real_part.view(), &ranks).map_err(|e| {
            QuantRS2Error::InvalidInput(format!("Tucker decomposition (real) failed: {e}"))
        })?;

        // Apply Tucker decomposition to imaginary part
        let tucker_imag = tucker_decomposition(&imag_part.view(), &ranks).map_err(|e| {
            QuantRS2Error::InvalidInput(format!("Tucker decomposition (imag) failed: {e}"))
        })?;

        // Convert cores to 2D
        let core_real = tucker_real
            .core
            .into_dimensionality::<scirs2_core::ndarray::Ix2>()
            .map_err(|e| {
                QuantRS2Error::InvalidInput(format!("Failed to convert real core: {e}"))
            })?;
        let core_imag = tucker_imag
            .core
            .into_dimensionality::<scirs2_core::ndarray::Ix2>()
            .map_err(|e| {
                QuantRS2Error::InvalidInput(format!("Failed to convert imag core: {e}"))
            })?;

        // Combine real and imaginary cores into complex core
        let core_2d = Array2::from_shape_fn(core_real.dim(), |(i, j)| {
            Complex64::new(core_real[(i, j)], core_imag[(i, j)])
        });

        // Combine real and imaginary factors into complex factors
        // Average the factors from real and imag decompositions (since they should be similar for unitary matrices)
        let mut factors = Vec::new();
        for i in 0..tucker_real.factors.len() {
            let factor_real = &tucker_real.factors[i];
            let factor_imag = &tucker_imag.factors[i];
            let combined_factor = Array2::from_shape_fn(factor_real.dim(), |(r, c)| {
                Complex64::new(factor_real[(r, c)], factor_imag[(r, c)])
            });
            factors.push(combined_factor);
        }

        // Check if compression is worthwhile
        let original_params = rows * cols * 2; // Complex numbers have 2 components
        let compressed_params =
            (core_2d.len() * 2) + factors.iter().map(|f| f.len() * 2).sum::<usize>();

        if compressed_params >= original_params * 3 / 4 {
            // Not enough compression benefit
            return Ok(None);
        }

        // Verify approximation quality by reconstructing and comparing
        let reconstructed = self.reconstruct_tucker(&core_2d, &factors)?;
        if !matrices_approx_equal(&reconstructed.view(), matrix, self.config.tolerance) {
            return Ok(None);
        }

        Ok(Some(CompressedGate::Tucker {
            core: core_2d,
            factors,
        }))
    }

    /// Reconstruct matrix from Tucker decomposition
    fn reconstruct_tucker(
        &self,
        core: &Array2<Complex64>,
        factors: &[Array2<Complex64>],
    ) -> QuantRS2Result<Array2<Complex64>> {
        if factors.len() != 2 {
            return Err(QuantRS2Error::InvalidInput(
                "Tucker decomposition requires exactly 2 factors for 2D matrices".to_string(),
            ));
        }

        // Reconstruct: M ≈ U_1 × core × U_2^T
        // First: temp = core × U_2^T
        let temp = core.dot(&factors[1].t());
        // Second: result = U_1 × temp
        let result = factors[0].dot(&temp);

        Ok(result)
    }

    /// Try to find parameterized representation
    fn try_parameterized_compression(
        &self,
        gate: &dyn GateOp,
    ) -> QuantRS2Result<Option<CompressedGate>> {
        // This would identify if the gate can be represented
        // as a parameterized gate (e.g., rotation gates)

        // For now, we'll use global optimization to find parameters
        let matrix_vec = gate.matrix()?;
        let n = (matrix_vec.len() as f64).sqrt() as usize;

        if n > 4 {
            // Only try for single and two-qubit gates
            return Ok(None);
        }

        // Convert vector to 2D array
        let mut target_matrix = Array2::zeros((n, n));
        for j in 0..n {
            for i in 0..n {
                target_matrix[(i, j)] = matrix_vec[j * n + i];
            }
        }

        let gate_type = self.identify_gate_type(gate);

        // Set up bounds for optimization
        let dim = match gate_type.as_str() {
            "rotation" => 3, // Three Euler angles
            "phase" => 1,    // One phase parameter
            _ => 6,          // General parameterization
        };
        let bounds = vec![(Some(-std::f64::consts::PI), Some(std::f64::consts::PI)); dim];

        // Clone values needed for the closure to avoid borrowing self
        let target_matrix_clone = target_matrix.clone();
        let gate_type_clone = gate_type.clone();
        // let _tolerance = self.config.tolerance;

        // Create objective function
        let objective = move |x: &scirs2_core::ndarray::ArrayView1<f64>| -> f64 {
            let params: Vec<f64> = x.iter().copied().collect();

            // Inline the evaluation logic since we can't access self
            let gate_matrix = match gate_type_clone.as_str() {
                "phase" => {
                    let mut matrix = Array2::eye(target_matrix_clone.dim().0);
                    if !params.is_empty() {
                        let phase = Complex64::from_polar(1.0, params[0]);
                        let n = matrix.dim().0;
                        matrix[(n - 1, n - 1)] = phase;
                    }
                    matrix
                }
                "rotation" | _ => Array2::eye(target_matrix_clone.dim().0), // Placeholder
            };

            // Compute Frobenius norm of difference
            let diff = &target_matrix_clone - &gate_matrix;
            diff.iter().map(|c| c.norm_sqr()).sum::<f64>().sqrt()
        };

        // Use differential evolution for global optimization
        let mut options = DifferentialEvolutionOptions::default();
        options.popsize = 50;
        options.maxiter = self.config.max_iterations;
        options.tol = self.config.tolerance;

        let de_bounds: Vec<(f64, f64)> = bounds
            .into_iter()
            .map(|(low, high)| {
                (
                    low.unwrap_or(-std::f64::consts::PI),
                    high.unwrap_or(std::f64::consts::PI),
                )
            })
            .collect();

        let result =
            differential_evolution(objective, &de_bounds, Some(options), None).map_err(|e| {
                QuantRS2Error::InvalidInput(format!("Parameter optimization failed: {e:?}"))
            })?;

        if result.fun > self.config.tolerance {
            // Optimization didn't converge well enough
            return Ok(None);
        }

        Ok(Some(CompressedGate::Parameterized {
            gate_type,
            parameters: result.x.to_vec(),
            qubits: vec![], // Would need to extract from gate
        }))
    }

    /// Merge adjacent gates that can be combined
    fn merge_adjacent_gates(
        &self,
        gates: &[Box<dyn GateOp>],
    ) -> QuantRS2Result<Vec<Box<dyn GateOp>>> {
        let mut merged = Vec::new();
        let mut i = 0;

        while i < gates.len() {
            if i + 1 < gates.len() {
                // Check if gates can be merged
                if self.can_merge(gates[i].as_ref(), gates[i + 1].as_ref()) {
                    // Merge the gates
                    let combined =
                        self.merge_two_gates(gates[i].as_ref(), gates[i + 1].as_ref())?;
                    merged.push(combined);
                    i += 2;
                } else {
                    merged.push(gates[i].clone_gate());
                    i += 1;
                }
            } else {
                merged.push(gates[i].clone_gate());
                i += 1;
            }
        }

        Ok(merged)
    }

    /// Check if two gates can be merged
    fn can_merge(&self, gate1: &dyn GateOp, gate2: &dyn GateOp) -> bool {
        // Gates can be merged if they:
        // 1. Act on the same qubits
        // 2. Are both unitary
        // 3. Their product is simpler than the individual gates

        // For now, simple check - same type gates on same qubits
        gate1.name() == gate2.name()
    }

    /// Merge two gates into one
    fn merge_two_gates(
        &self,
        gate1: &dyn GateOp,
        gate2: &dyn GateOp,
    ) -> QuantRS2Result<Box<dyn GateOp>> {
        // Get matrices
        let matrix1_vec = gate1.matrix()?;
        let matrix2_vec = gate2.matrix()?;

        // Convert to 2D arrays
        let n = (matrix1_vec.len() as f64).sqrt() as usize;
        let mut matrix1 = Array2::zeros((n, n));
        let mut matrix2 = Array2::zeros((n, n));

        for j in 0..n {
            for i in 0..n {
                matrix1[(i, j)] = matrix1_vec[j * n + i];
                matrix2[(i, j)] = matrix2_vec[j * n + i];
            }
        }

        // Matrix multiplication
        let combined_matrix = matrix2.dot(&matrix1);

        // Create a custom gate with the combined matrix
        Ok(Box::new(CustomGate::new(
            format!("{}_{}_merged", gate1.name(), gate2.name()),
            combined_matrix,
        )))
    }

    /// Compute hash of matrix for caching
    fn compute_matrix_hash(&self, matrix: &ArrayView2<Complex64>) -> u64 {
        use std::collections::hash_map::DefaultHasher;
        use std::hash::{Hash, Hasher};

        let mut hasher = DefaultHasher::new();
        for elem in matrix {
            // Hash real and imaginary parts
            elem.re.to_bits().hash(&mut hasher);
            elem.im.to_bits().hash(&mut hasher);
        }
        hasher.finish()
    }

    /// Find effective rank based on singular values
    fn find_effective_rank(
        &self,
        s_real: &scirs2_core::ndarray::Array1<f64>,
        s_imag: &scirs2_core::ndarray::Array1<f64>,
    ) -> QuantRS2Result<usize> {
        let max_singular = s_real
            .iter()
            .chain(s_imag.iter())
            .map(|s| s.abs())
            .fold(0.0, f64::max);

        let threshold = max_singular * self.config.tolerance;

        let rank = s_real
            .iter()
            .zip(s_imag.iter())
            .take_while(|(sr, si)| sr.abs() > threshold || si.abs() > threshold)
            .count();

        Ok(rank.max(1))
    }

    /// Combine real and imaginary parts into complex matrix
    fn combine_complex(
        &self,
        real: &Array2<f64>,
        imag: &Array2<f64>,
        rank: usize,
    ) -> QuantRS2Result<Array2<Complex64>> {
        let (rows, _) = real.dim();
        let result = Array2::from_shape_fn((rows, rank), |(i, j)| {
            Complex64::new(real[(i, j)], imag[(i, j)])
        });
        Ok(result)
    }

    /// Combine with singular values
    fn combine_complex_with_singular(
        &self,
        vt_real: &Array2<f64>,
        vt_imag: &Array2<f64>,
        s_real: &scirs2_core::ndarray::Array1<f64>,
        s_imag: &scirs2_core::ndarray::Array1<f64>,
        rank: usize,
    ) -> QuantRS2Result<Array2<Complex64>> {
        let (_, cols) = vt_real.dim();
        let result = Array2::from_shape_fn((rank, cols), |(i, j)| {
            let s = Complex64::new(s_real[i], s_imag[i]);
            let v = Complex64::new(vt_real[(i, j)], vt_imag[(i, j)]);
            s * v
        });
        Ok(result)
    }

    /// Convert tensor data to complex matrix
    #[allow(dead_code)]
    fn tensor_to_complex_matrix(&self, tensor: &[f64]) -> QuantRS2Result<Array2<Complex64>> {
        let size = (tensor.len() / 2) as f64;
        let dim = size.sqrt() as usize;

        let mut matrix = Array2::zeros((dim, dim));
        for i in 0..dim {
            for j in 0..dim {
                let idx = (i * dim + j) * 2;
                matrix[(i, j)] = Complex64::new(tensor[idx], tensor[idx + 1]);
            }
        }

        Ok(matrix)
    }

    /// Convert ArrayD to complex matrix
    #[allow(dead_code)]
    fn tensor_to_complex_matrix_from_array(
        &self,
        tensor: &scirs2_core::ndarray::ArrayD<f64>,
    ) -> QuantRS2Result<Array2<Complex64>> {
        // For now, just flatten the tensor and reshape to square matrix
        let elements: Vec<f64> = tensor.iter().copied().collect();
        let size = elements.len() as f64;
        let dim = size.sqrt() as usize;

        if dim * dim == elements.len() {
            let mut matrix = Array2::zeros((dim, dim));
            for i in 0..dim {
                for j in 0..dim {
                    let idx = i * dim + j;
                    matrix[(i, j)] = Complex64::new(elements[idx], 0.0);
                }
            }
            Ok(matrix)
        } else {
            // If not square, pad with zeros
            let dim = (size.sqrt().ceil()) as usize;
            let mut matrix = Array2::zeros((dim, dim));
            for (idx, &val) in elements.iter().enumerate() {
                let i = idx / dim;
                let j = idx % dim;
                if i < dim && j < dim {
                    matrix[(i, j)] = Complex64::new(val, 0.0);
                }
            }
            Ok(matrix)
        }
    }

    /// Identify gate type for parameterization
    fn identify_gate_type(&self, gate: &dyn GateOp) -> String {
        // Simple heuristic based on gate name
        let name = gate.name();
        if name.contains("rot") || name.contains("Rot") {
            "rotation".to_string()
        } else if name.contains("phase") || name.contains("Phase") {
            "phase".to_string()
        } else {
            "general".to_string()
        }
    }

    /// Evaluate gate parameters for optimization
    #[allow(dead_code)]
    fn evaluate_gate_parameters(
        &self,
        target: &Array2<Complex64>,
        gate_type: &str,
        params: &[f64],
    ) -> f64 {
        // Construct gate from parameters
        let gate_matrix = match gate_type {
            "rotation" => self.rotation_matrix_from_params(params, target.dim().0),
            "phase" => self.phase_matrix_from_params(params, target.dim().0),
            _ => self.general_matrix_from_params(params, target.dim().0),
        };

        // Compute Frobenius norm of difference
        let diff = target - &gate_matrix;
        diff.iter().map(|c| c.norm_sqr()).sum::<f64>().sqrt()
    }

    fn rotation_matrix_from_params(&self, _params: &[f64], dim: usize) -> Array2<Complex64> {
        // Construct rotation matrix from Euler angles
        // This is a placeholder - would need proper implementation
        Array2::eye(dim)
    }

    fn phase_matrix_from_params(&self, params: &[f64], dim: usize) -> Array2<Complex64> {
        let mut matrix = Array2::eye(dim);
        if !params.is_empty() {
            let phase = Complex64::from_polar(1.0, params[0]);
            matrix[(dim - 1, dim - 1)] = phase;
        }
        matrix
    }

    #[allow(dead_code)]
    fn general_matrix_from_params(&self, _params: &[f64], dim: usize) -> Array2<Complex64> {
        // General parameterization - would need proper implementation
        Array2::eye(dim)
    }

    /// Try runtime compression using various algorithms
    fn try_runtime_compression(&self, gate: &dyn GateOp) -> QuantRS2Result<Option<CompressedGate>> {
        let matrix_vec = gate.matrix()?;
        let n = (matrix_vec.len() as f64).sqrt() as usize;

        // Create gate metadata
        let metadata = GateMetadata {
            name: gate.name().to_string(),
            num_qubits: gate.num_qubits(),
            qubits: gate.qubits(),
            matrix_dims: (n, n),
            sparsity_ratio: self.calculate_sparsity_ratio(&matrix_vec),
            is_unitary: self.check_unitary(&matrix_vec, n),
        };

        // Serialize the matrix to bytes
        let matrix_bytes = self.serialize_matrix(&matrix_vec)?;

        // Try different compression algorithms
        let best_compression = self.find_best_compression(&matrix_bytes, &metadata)?;

        // Only compress if we achieve significant compression ratio
        if best_compression.compression_ratio < 0.8 {
            Ok(Some(CompressedGate::RuntimeCompressed {
                compressed_data: best_compression.data,
                compression_type: best_compression.compression_type,
                original_size: matrix_bytes.len(),
                gate_metadata: metadata,
            }))
        } else {
            Ok(None)
        }
    }

    /// Calculate sparsity ratio of a matrix
    fn calculate_sparsity_ratio(&self, matrix: &[Complex64]) -> f64 {
        let zero_count = matrix
            .iter()
            .filter(|&c| c.norm() < self.config.tolerance)
            .count();
        zero_count as f64 / matrix.len() as f64
    }

    /// Check if matrix is unitary
    fn check_unitary(&self, matrix: &[Complex64], n: usize) -> bool {
        // Simple check: ||U†U - I||_F < tolerance
        // This is a simplified implementation
        if n > 8 {
            return false; // Skip check for large matrices
        }

        // Convert to Array2 for easier computation
        let mut u = Array2::zeros((n, n));
        for j in 0..n {
            for i in 0..n {
                u[(i, j)] = matrix[j * n + i];
            }
        }

        // Compute U†U
        let u_dagger = u.t().mapv(|c| c.conj());
        let product = u_dagger.dot(&u);

        // Check if close to identity
        let identity = Array2::<Complex64>::eye(n);
        let diff = &product - &identity;
        let frobenius_norm: f64 = diff.iter().map(|c| c.norm_sqr()).sum::<f64>().sqrt();

        frobenius_norm < self.config.tolerance
    }

    /// Serialize matrix to bytes
    fn serialize_matrix(&self, matrix: &[Complex64]) -> QuantRS2Result<Vec<u8>> {
        let mut bytes = Vec::with_capacity(matrix.len() * 16); // 16 bytes per Complex64

        for &complex in matrix {
            bytes.extend_from_slice(&complex.re.to_le_bytes());
            bytes.extend_from_slice(&complex.im.to_le_bytes());
        }

        Ok(bytes)
    }

    /// Find the best compression algorithm for the data
    fn find_best_compression(
        &self,
        data: &[u8],
        metadata: &GateMetadata,
    ) -> QuantRS2Result<CompressionResult> {
        let mut best_compression = CompressionResult {
            data: data.to_vec(),
            compression_type: CompressionType::None,
            compression_ratio: 1.0,
        };

        // Try Zlib compression
        if let Ok(zlib_compressed) = self.compress_zlib(data) {
            let ratio = zlib_compressed.len() as f64 / data.len() as f64;
            if ratio < best_compression.compression_ratio {
                best_compression = CompressionResult {
                    data: zlib_compressed,
                    compression_type: CompressionType::Zlib,
                    compression_ratio: ratio,
                };
            }
        }

        // Try LZ4 compression (simulated - would use actual LZ4 in real implementation)
        if let Ok(lz4_compressed) = self.compress_lz4(data) {
            let ratio = lz4_compressed.len() as f64 / data.len() as f64;
            if ratio < best_compression.compression_ratio {
                best_compression = CompressionResult {
                    data: lz4_compressed,
                    compression_type: CompressionType::LZ4,
                    compression_ratio: ratio,
                };
            }
        }

        // Try quantum-optimized compression for sparse matrices
        if metadata.sparsity_ratio > 0.3 {
            if let Ok(quantum_compressed) = self.compress_quantum_optimized(data, metadata) {
                let ratio = quantum_compressed.len() as f64 / data.len() as f64;
                if ratio < best_compression.compression_ratio {
                    best_compression = CompressionResult {
                        data: quantum_compressed,
                        compression_type: CompressionType::QuantumOptimized,
                        compression_ratio: ratio,
                    };
                }
            }
        }

        Ok(best_compression)
    }

    /// Compress using Zlib
    fn compress_zlib(&self, data: &[u8]) -> QuantRS2Result<Vec<u8>> {
        #[cfg(feature = "compression")]
        {
            oxiarc_deflate::zlib::zlib_compress(data, 6)
                .map_err(|e| QuantRS2Error::RuntimeError(format!("Zlib compression failed: {e}")))
        }

        #[cfg(not(feature = "compression"))]
        {
            // Fallback: return uncompressed data
            Ok(data.to_vec())
        }
    }

    /// Compress using LZ4 (simulated)
    fn compress_lz4(&self, data: &[u8]) -> QuantRS2Result<Vec<u8>> {
        // This is a simplified simulation of LZ4 compression
        // In a real implementation, you would use the lz4 crate

        // Simple run-length encoding as a placeholder
        let mut compressed = Vec::new();
        let mut i = 0;

        while i < data.len() {
            let byte = data[i];
            let mut count = 1;

            while i + count < data.len() && data[i + count] == byte && count < 255 {
                count += 1;
            }

            if count > 3 {
                // Run-length encode
                compressed.push(0xFF); // Marker for run-length
                compressed.push(count as u8);
                compressed.push(byte);
            } else {
                // Just copy the bytes
                for _ in 0..count {
                    compressed.push(byte);
                }
            }

            i += count;
        }

        Ok(compressed)
    }

    /// Quantum-optimized compression for sparse matrices
    fn compress_quantum_optimized(
        &self,
        data: &[u8],
        metadata: &GateMetadata,
    ) -> QuantRS2Result<Vec<u8>> {
        // Custom compression for quantum gate matrices
        let mut compressed = Vec::new();

        // Add metadata header
        compressed.extend_from_slice(&(metadata.num_qubits as u32).to_le_bytes());
        compressed.extend_from_slice(&metadata.sparsity_ratio.to_le_bytes());

        // For sparse matrices, store only non-zero elements with their indices
        if metadata.sparsity_ratio > 0.5 {
            let complex_data = self.deserialize_matrix_from_bytes(data)?;
            // let _n = metadata.matrix_dims.0;

            // Store as (index, real, imag) triples for non-zero elements
            let mut non_zero_count = 0u32;
            let mut non_zero_data = Vec::new();

            for (idx, &complex) in complex_data.iter().enumerate() {
                if complex.norm() >= self.config.tolerance {
                    non_zero_data.extend_from_slice(&(idx as u32).to_le_bytes());
                    non_zero_data.extend_from_slice(&complex.re.to_le_bytes());
                    non_zero_data.extend_from_slice(&complex.im.to_le_bytes());
                    non_zero_count += 1;
                }
            }

            compressed.extend_from_slice(&non_zero_count.to_le_bytes());
            compressed.extend_from_slice(&non_zero_data);
        } else {
            // For dense matrices, use delta encoding
            compressed.extend_from_slice(data);
        }

        Ok(compressed)
    }

    /// Deserialize matrix from bytes
    fn deserialize_matrix_from_bytes(&self, bytes: &[u8]) -> QuantRS2Result<Vec<Complex64>> {
        if bytes.len() % 16 != 0 {
            return Err(QuantRS2Error::InvalidInput(
                "Invalid byte length for Complex64 array".to_string(),
            ));
        }

        let mut matrix = Vec::with_capacity(bytes.len() / 16);

        for chunk in bytes.chunks_exact(16) {
            let re = f64::from_le_bytes([
                chunk[0], chunk[1], chunk[2], chunk[3], chunk[4], chunk[5], chunk[6], chunk[7],
            ]);
            let im = f64::from_le_bytes([
                chunk[8], chunk[9], chunk[10], chunk[11], chunk[12], chunk[13], chunk[14],
                chunk[15],
            ]);

            matrix.push(Complex64::new(re, im));
        }

        Ok(matrix)
    }

    /// Decompress runtime-compressed gate
    pub fn decompress_gate(&self, compressed: &CompressedGate) -> QuantRS2Result<Box<dyn GateOp>> {
        match compressed {
            CompressedGate::RuntimeCompressed {
                compressed_data,
                compression_type,
                original_size,
                gate_metadata,
            } => {
                // Decompress the data
                let decompressed_bytes = self.decompress_data(
                    compressed_data,
                    *compression_type,
                    *original_size,
                    gate_metadata,
                )?;

                // Deserialize back to matrix
                let matrix = self.deserialize_matrix_from_bytes(&decompressed_bytes)?;

                // Create a custom gate with the decompressed matrix
                let n = gate_metadata.matrix_dims.0;
                let mut matrix_2d = Array2::zeros((n, n));
                for j in 0..n {
                    for i in 0..n {
                        matrix_2d[(i, j)] = matrix[j * n + i];
                    }
                }

                Ok(Box::new(CustomGate::with_qubits(
                    gate_metadata.name.clone(),
                    matrix_2d,
                    gate_metadata.qubits.clone(),
                )))
            }
            CompressedGate::LowRank { left, right, .. } => {
                // Reconstruct from low-rank approximation
                let reconstructed = left.dot(&right.t());
                Ok(Box::new(CustomGate::new(
                    "LowRank".to_string(),
                    reconstructed,
                )))
            }
            CompressedGate::Tucker { core, factors } => {
                // Reconstruct from Tucker decomposition using proper tensor contraction
                let reconstructed = self.reconstruct_tucker(core, factors)?;
                Ok(Box::new(CustomGate::new(
                    "Tucker".to_string(),
                    reconstructed,
                )))
            }
            CompressedGate::Parameterized {
                gate_type,
                parameters,
                qubits,
            } => {
                // Reconstruct parameterized gate
                self.reconstruct_parameterized_gate(gate_type, parameters, qubits)
            }
            CompressedGate::Original(gate) => Ok(gate.clone_gate()),
        }
    }

    /// Decompress data based on compression type
    fn decompress_data(
        &self,
        compressed_data: &[u8],
        compression_type: CompressionType,
        original_size: usize,
        metadata: &GateMetadata,
    ) -> QuantRS2Result<Vec<u8>> {
        match compression_type {
            CompressionType::None => Ok(compressed_data.to_vec()),
            CompressionType::Zlib => self.decompress_zlib(compressed_data),
            CompressionType::LZ4 => self.decompress_lz4(compressed_data, original_size),
            CompressionType::QuantumOptimized => {
                self.decompress_quantum_optimized(compressed_data, metadata)
            }
            CompressionType::Huffman => {
                // Placeholder for Huffman decompression
                Ok(compressed_data.to_vec())
            }
        }
    }

    /// Decompress Zlib data
    fn decompress_zlib(&self, compressed_data: &[u8]) -> QuantRS2Result<Vec<u8>> {
        #[cfg(feature = "compression")]
        {
            oxiarc_deflate::zlib::zlib_decompress(compressed_data)
                .map_err(|e| QuantRS2Error::RuntimeError(format!("Zlib decompression failed: {e}")))
        }

        #[cfg(not(feature = "compression"))]
        {
            // Fallback: return compressed data as-is (since we didn't really compress it)
            Ok(compressed_data.to_vec())
        }
    }

    /// Decompress LZ4 data (simulated)
    fn decompress_lz4(
        &self,
        compressed_data: &[u8],
        original_size: usize,
    ) -> QuantRS2Result<Vec<u8>> {
        // Reverse of the simple run-length encoding
        let mut decompressed = Vec::with_capacity(original_size);
        let mut i = 0;

        while i < compressed_data.len() {
            if compressed_data[i] == 0xFF && i + 2 < compressed_data.len() {
                // Run-length encoded
                let count = compressed_data[i + 1] as usize;
                let byte = compressed_data[i + 2];

                for _ in 0..count {
                    decompressed.push(byte);
                }

                i += 3;
            } else {
                // Regular byte
                decompressed.push(compressed_data[i]);
                i += 1;
            }
        }

        Ok(decompressed)
    }

    /// Decompress quantum-optimized data
    fn decompress_quantum_optimized(
        &self,
        compressed_data: &[u8],
        metadata: &GateMetadata,
    ) -> QuantRS2Result<Vec<u8>> {
        if compressed_data.len() < 12 {
            return Err(QuantRS2Error::InvalidInput(
                "Invalid quantum-optimized compressed data".to_string(),
            ));
        }

        let mut cursor = 0;

        // Read header
        let _num_qubits = u32::from_le_bytes([
            compressed_data[cursor],
            compressed_data[cursor + 1],
            compressed_data[cursor + 2],
            compressed_data[cursor + 3],
        ]);
        cursor += 4;

        let sparsity_ratio = f64::from_le_bytes([
            compressed_data[cursor],
            compressed_data[cursor + 1],
            compressed_data[cursor + 2],
            compressed_data[cursor + 3],
            compressed_data[cursor + 4],
            compressed_data[cursor + 5],
            compressed_data[cursor + 6],
            compressed_data[cursor + 7],
        ]);
        cursor += 8;

        if sparsity_ratio > 0.5 {
            // Sparse format: reconstruct from (index, real, imag) triples
            let non_zero_count = u32::from_le_bytes([
                compressed_data[cursor],
                compressed_data[cursor + 1],
                compressed_data[cursor + 2],
                compressed_data[cursor + 3],
            ]);
            cursor += 4;

            let matrix_size = metadata.matrix_dims.0 * metadata.matrix_dims.1;
            let mut matrix = vec![Complex64::new(0.0, 0.0); matrix_size];

            for _ in 0..non_zero_count {
                let index = u32::from_le_bytes([
                    compressed_data[cursor],
                    compressed_data[cursor + 1],
                    compressed_data[cursor + 2],
                    compressed_data[cursor + 3],
                ]) as usize;
                cursor += 4;

                let re = f64::from_le_bytes([
                    compressed_data[cursor],
                    compressed_data[cursor + 1],
                    compressed_data[cursor + 2],
                    compressed_data[cursor + 3],
                    compressed_data[cursor + 4],
                    compressed_data[cursor + 5],
                    compressed_data[cursor + 6],
                    compressed_data[cursor + 7],
                ]);
                cursor += 8;

                let im = f64::from_le_bytes([
                    compressed_data[cursor],
                    compressed_data[cursor + 1],
                    compressed_data[cursor + 2],
                    compressed_data[cursor + 3],
                    compressed_data[cursor + 4],
                    compressed_data[cursor + 5],
                    compressed_data[cursor + 6],
                    compressed_data[cursor + 7],
                ]);
                cursor += 8;

                if index < matrix_size {
                    matrix[index] = Complex64::new(re, im);
                }
            }

            // Serialize back to bytes
            self.serialize_matrix(&matrix)
        } else {
            // Dense format: just return the remaining data
            Ok(compressed_data[cursor..].to_vec())
        }
    }

    /// Reconstruct parameterized gate
    fn reconstruct_parameterized_gate(
        &self,
        gate_type: &str,
        parameters: &[f64],
        qubits: &[QubitId],
    ) -> QuantRS2Result<Box<dyn GateOp>> {
        match gate_type {
            "rotation" => {
                if parameters.len() >= 3 && !qubits.is_empty() {
                    // Create a rotation gate from Euler angles
                    let matrix = self.rotation_matrix_from_params(parameters, 2);
                    Ok(Box::new(CustomGate::with_qubits(
                        "Rotation".to_string(),
                        matrix,
                        qubits.to_vec(),
                    )))
                } else {
                    Err(QuantRS2Error::InvalidInput(
                        "Invalid rotation parameters".to_string(),
                    ))
                }
            }
            "phase" => {
                if !parameters.is_empty() && !qubits.is_empty() {
                    let matrix = self.phase_matrix_from_params(parameters, 2);
                    Ok(Box::new(CustomGate::with_qubits(
                        "Phase".to_string(),
                        matrix,
                        qubits.to_vec(),
                    )))
                } else {
                    Err(QuantRS2Error::InvalidInput(
                        "Invalid phase parameters".to_string(),
                    ))
                }
            }
            _ => {
                // General case - create identity for now
                let dim = 1 << qubits.len();
                let matrix = Array2::eye(dim);
                Ok(Box::new(CustomGate::with_qubits(
                    gate_type.to_string(),
                    matrix,
                    qubits.to_vec(),
                )))
            }
        }
    }
}

/// Result of compression operation
struct CompressionResult {
    data: Vec<u8>,
    compression_type: CompressionType,
    compression_ratio: f64,
}

/// Custom gate implementation for compressed gates
#[derive(Debug, Clone)]
pub struct CustomGate {
    name: String,
    matrix: Array2<Complex64>,
    qubits: Vec<QubitId>,
}

impl CustomGate {
    pub fn new(name: String, matrix: Array2<Complex64>) -> Self {
        // Determine number of qubits from matrix size
        let n_qubits = (matrix.dim().0 as f64).log2() as usize;
        let qubits = (0..n_qubits).map(|i| QubitId::new(i as u32)).collect();
        Self {
            name,
            matrix,
            qubits,
        }
    }

    pub const fn with_qubits(
        name: String,
        matrix: Array2<Complex64>,
        qubits: Vec<QubitId>,
    ) -> Self {
        Self {
            name,
            matrix,
            qubits,
        }
    }
}

impl GateOp for CustomGate {
    fn name(&self) -> &'static str {
        // Since we need 'static, we leak the string
        Box::leak(self.name.clone().into_boxed_str())
    }

    fn qubits(&self) -> Vec<QubitId> {
        self.qubits.clone()
    }

    fn matrix(&self) -> QuantRS2Result<Vec<Complex64>> {
        // Flatten the matrix to a vector in column-major order
        let mut result = Vec::with_capacity(self.matrix.len());
        let (rows, cols) = self.matrix.dim();
        for j in 0..cols {
            for i in 0..rows {
                result.push(self.matrix[(i, j)]);
            }
        }
        Ok(result)
    }

    fn as_any(&self) -> &dyn Any {
        self
    }

    fn clone_gate(&self) -> Box<dyn GateOp> {
        Box::new(self.clone())
    }
}

/// Compression statistics
#[derive(Debug, Clone, Default)]
pub struct CompressionStats {
    pub original_gates: usize,
    pub compressed_gates: usize,
    pub low_rank_compressions: usize,
    pub tucker_compressions: usize,
    pub parameterized_compressions: usize,
    pub compression_ratio: f64,
    pub total_parameters_before: usize,
    pub total_parameters_after: usize,
}

impl GateSequenceCompressor {
    /// Get compression statistics
    pub fn get_stats(
        &self,
        original: &[Box<dyn GateOp>],
        compressed: &[CompressedGate],
    ) -> CompressionStats {
        let mut stats = CompressionStats::default();
        stats.original_gates = original.len();
        stats.compressed_gates = compressed.len();

        for gate in compressed {
            match gate {
                CompressedGate::LowRank { left, right, .. } => {
                    stats.low_rank_compressions += 1;
                    stats.total_parameters_after += (left.len() + right.len()) * 2;
                }
                CompressedGate::Tucker { core, factors } => {
                    stats.tucker_compressions += 1;
                    stats.total_parameters_after += core.len() * 2;
                    stats.total_parameters_after +=
                        factors.iter().map(|f| f.len() * 2).sum::<usize>();
                }
                CompressedGate::Parameterized { parameters, .. } => {
                    stats.parameterized_compressions += 1;
                    stats.total_parameters_after += parameters.len();
                }
                CompressedGate::RuntimeCompressed {
                    compressed_data, ..
                } => {
                    // For runtime compressed gates, count the compressed data size
                    stats.total_parameters_after += compressed_data.len();
                }
                CompressedGate::Original(gate) => {
                    if let Ok(matrix_vec) = gate.matrix() {
                        let size = (matrix_vec.len() as f64).sqrt() as usize;
                        stats.total_parameters_after += size * size * 2;
                    }
                }
            }
        }

        for gate in original {
            if let Ok(matrix_vec) = gate.matrix() {
                let size = (matrix_vec.len() as f64).sqrt() as usize;
                stats.total_parameters_before += size * size * 2;
            }
        }

        stats.compression_ratio = if stats.total_parameters_before > 0 {
            stats.total_parameters_after as f64 / stats.total_parameters_before as f64
        } else {
            1.0
        };

        stats
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::gate::single::{Hadamard, PauliX, PauliZ};
    use crate::qubit::QubitId;

    #[test]
    fn test_gate_compression() {
        let config = CompressionConfig::default();
        let mut compressor = GateSequenceCompressor::new(config);

        // Test single gate compression
        let h_gate = Hadamard {
            target: QubitId::new(0),
        };
        let compressed = compressor
            .compress_gate(&h_gate)
            .expect("Failed to compress Hadamard gate");

        match compressed {
            CompressedGate::Original(_) => {
                // H gate is already minimal, shouldn't compress
            }
            CompressedGate::RuntimeCompressed { .. } => {
                // H gate might be runtime compressed, which is acceptable
            }
            _ => panic!("H gate shouldn't be significantly compressed"),
        }
    }

    #[test]
    fn test_sequence_compression() {
        let config = CompressionConfig::default();
        let mut compressor = GateSequenceCompressor::new(config);

        // Create a sequence of gates
        let gates: Vec<Box<dyn GateOp>> = vec![
            Box::new(Hadamard {
                target: QubitId::new(0),
            }),
            Box::new(PauliX {
                target: QubitId::new(0),
            }),
            Box::new(Hadamard {
                target: QubitId::new(0),
            }),
        ];

        let compressed = compressor
            .compress_sequence(&gates)
            .expect("Failed to compress gate sequence");
        assert!(compressed.len() <= gates.len());
    }

    #[test]
    fn test_compression_stats() {
        let config = CompressionConfig::default();
        let mut compressor = GateSequenceCompressor::new(config);

        let gates: Vec<Box<dyn GateOp>> = vec![
            Box::new(Hadamard {
                target: QubitId::new(0),
            }),
            Box::new(PauliZ {
                target: QubitId::new(0),
            }),
        ];

        let compressed = compressor
            .compress_sequence(&gates)
            .expect("Failed to compress gate sequence for stats");
        let stats = compressor.get_stats(&gates, &compressed);

        assert_eq!(stats.original_gates, 2);
        // Compression ratio can be > 1.0 for small gates due to overhead
        assert!(stats.compression_ratio >= 0.0);
    }
}