quantrs2_sim/

enhanced_tensor_networks.rs

//! Enhanced tensor network simulation with advanced contraction heuristics.
//!
//! This module implements state-of-the-art tensor network algorithms for
//! quantum circuit simulation, including advanced contraction optimization,
//! bond dimension management, and SciRS2-accelerated tensor operations.

use ndarray::{Array, Array2, ArrayD, IxDyn};
use num_complex::Complex64;
use scirs2_core::parallel_ops::*;
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};

use crate::error::{Result, SimulatorError};
use crate::scirs2_integration::SciRS2Backend;

#[cfg(feature = "advanced_math")]
/// Placeholder for contraction optimizer
pub struct ContractionOptimizer {
    strategy: String,
}

#[cfg(feature = "advanced_math")]
impl ContractionOptimizer {
    pub fn new() -> Result<Self> {
        Ok(Self {
            strategy: "default".to_string(),
        })
    }
}

// Note: scirs2_linalg types temporarily unavailable
// #[cfg(feature = "advanced_math")]
// use scirs2_linalg::{BondDimension, TensorNetwork};

/// Advanced tensor network configuration
#[derive(Debug, Clone)]
pub struct EnhancedTensorNetworkConfig {
    /// Maximum bond dimension allowed
    pub max_bond_dimension: usize,
    /// Contraction optimization strategy
    pub contraction_strategy: ContractionStrategy,
    /// Memory limit for tensor operations (bytes)
    pub memory_limit: usize,
    /// Enable approximate contractions
    pub enable_approximations: bool,
    /// SVD truncation threshold
    pub svd_threshold: f64,
    /// Maximum optimization time per contraction
    pub max_optimization_time_ms: u64,
    /// Enable parallel tensor operations
    pub parallel_contractions: bool,
    /// Use SciRS2 acceleration
    pub use_scirs2_acceleration: bool,
    /// Enable tensor slicing for large networks
    pub enable_slicing: bool,
    /// Maximum number of slices
    pub max_slices: usize,
}

impl Default for EnhancedTensorNetworkConfig {
    fn default() -> Self {
        Self {
            max_bond_dimension: 1024,
            contraction_strategy: ContractionStrategy::Adaptive,
            memory_limit: 16_000_000_000, // 16GB
            enable_approximations: true,
            svd_threshold: 1e-12,
            max_optimization_time_ms: 5000,
            parallel_contractions: true,
            use_scirs2_acceleration: true,
            enable_slicing: true,
            max_slices: 64,
        }
    }
}

/// Tensor network contraction strategies
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ContractionStrategy {
    /// Greedy local optimization
    Greedy,
    /// Dynamic programming global optimization
    DynamicProgramming,
    /// Simulated annealing optimization
    SimulatedAnnealing,
    /// Tree decomposition based
    TreeDecomposition,
    /// Adaptive strategy selection
    Adaptive,
    /// Machine learning guided
    MLGuided,
}

/// Tensor representation with enhanced metadata
#[derive(Debug, Clone)]
pub struct EnhancedTensor {
    /// Tensor data
    pub data: ArrayD<Complex64>,
    /// Index labels for contraction
    pub indices: Vec<TensorIndex>,
    /// Bond dimensions for each index
    pub bond_dimensions: Vec<usize>,
    /// Tensor ID for tracking
    pub id: usize,
    /// Memory footprint estimate
    pub memory_size: usize,
    /// Contraction cost estimate
    pub contraction_cost: f64,
    /// Priority for contraction ordering
    pub priority: f64,
}

/// Tensor index with enhanced information
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct TensorIndex {
    /// Index label
    pub label: String,
    /// Index dimension
    pub dimension: usize,
    /// Index type (physical, virtual, etc.)
    pub index_type: IndexType,
    /// Connected tensor IDs
    pub connected_tensors: Vec<usize>,
}

/// Types of tensor indices
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum IndexType {
    /// Physical qubit index
    Physical,
    /// Virtual bond index
    Virtual,
    /// Auxiliary index for decomposition
    Auxiliary,
    /// Time evolution index
    Temporal,
}

/// Machine learning predicted strategy
#[derive(Debug, Clone)]
pub struct MLPrediction {
    pub strategy: MLPredictedStrategy,
    pub confidence: f64,
    pub expected_performance: f64,
}

/// Predicted optimization strategies from ML model
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MLPredictedStrategy {
    DynamicProgramming,
    SimulatedAnnealing,
    TreeDecomposition,
    Greedy,
}

/// Network features for ML prediction
#[derive(Debug, Clone)]
pub struct NetworkFeatures {
    pub num_tensors: usize,
    pub connectivity_density: f64,
    pub max_bond_dimension: usize,
    pub avg_tensor_rank: f64,
    pub circuit_depth_estimate: usize,
    pub locality_score: f64,
    pub symmetry_score: f64,
}

/// Tree decomposition structure
#[derive(Debug, Clone)]
pub struct TreeDecomposition {
    pub bags: Vec<TreeBag>,
    pub treewidth: usize,
    pub root_bag: usize,
}

/// Individual bag in tree decomposition
#[derive(Debug, Clone)]
pub struct TreeBag {
    pub id: usize,
    pub tensors: Vec<usize>,
    pub parent: Option<usize>,
    pub children: Vec<usize>,
    pub separator: Vec<String>, // Common indices with parent
}

/// Adjacency graph for tensor networks
#[derive(Debug, Clone)]
pub struct TensorAdjacencyGraph {
    pub nodes: Vec<usize>,                        // Tensor IDs
    pub edges: HashMap<usize, Vec<(usize, f64)>>, // (neighbor, edge_weight)
    pub edge_weights: HashMap<(usize, usize), f64>,
}

/// Contraction path with detailed cost analysis
#[derive(Debug, Clone)]
pub struct EnhancedContractionPath {
    /// Sequence of tensor pairs to contract
    pub steps: Vec<ContractionStep>,
    /// Total computational cost estimate
    pub total_flops: f64,
    /// Maximum memory requirement
    pub peak_memory: usize,
    /// Contraction tree structure
    pub contraction_tree: ContractionTree,
    /// Parallelization opportunities
    pub parallel_sections: Vec<ParallelSection>,
}

/// Single contraction step
#[derive(Debug, Clone)]
pub struct ContractionStep {
    /// IDs of tensors to contract
    pub tensor_ids: (usize, usize),
    /// Resulting tensor ID
    pub result_id: usize,
    /// FLOP count for this step
    pub flops: f64,
    /// Memory required for this step
    pub memory_required: usize,
    /// Expected result dimensions
    pub result_dimensions: Vec<usize>,
    /// Can be parallelized
    pub parallelizable: bool,
}

/// Contraction tree for hierarchical optimization
#[derive(Debug, Clone)]
pub enum ContractionTree {
    /// Leaf node (original tensor)
    Leaf { tensor_id: usize },
    /// Internal node (contraction)
    Branch {
        left: Box<ContractionTree>,
        right: Box<ContractionTree>,
        contraction_cost: f64,
        result_bond_dim: usize,
    },
}

/// Parallel contraction section
#[derive(Debug, Clone)]
pub struct ParallelSection {
    /// Steps that can be executed in parallel
    pub parallel_steps: Vec<usize>,
    /// Dependencies between steps
    pub dependencies: HashMap<usize, Vec<usize>>,
    /// Expected speedup factor
    pub speedup_factor: f64,
}

/// Enhanced tensor network simulator
pub struct EnhancedTensorNetworkSimulator {
    /// Configuration
    config: EnhancedTensorNetworkConfig,
    /// Current tensor network
    network: TensorNetwork,
    /// SciRS2 backend
    backend: Option<SciRS2Backend>,
    /// Contraction optimizer
    #[cfg(feature = "advanced_math")]
    optimizer: Option<ContractionOptimizer>,
    /// Tensor cache for reused patterns
    tensor_cache: HashMap<String, EnhancedTensor>,
    /// Performance statistics
    stats: TensorNetworkStats,
}

/// Tensor network performance statistics
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct TensorNetworkStats {
    /// Total number of contractions performed
    pub total_contractions: usize,
    /// Total FLOP count
    pub total_flops: f64,
    /// Peak memory usage
    pub peak_memory_bytes: usize,
    /// Total execution time
    pub total_execution_time_ms: f64,
    /// Contraction optimization time
    pub optimization_time_ms: f64,
    /// Average bond dimension
    pub average_bond_dimension: f64,
    /// SVD truncation count
    pub svd_truncations: usize,
    /// Cache hit rate
    pub cache_hit_rate: f64,
}

/// Tensor network with enhanced contraction capabilities
struct TensorNetwork {
    /// Collection of tensors
    tensors: HashMap<usize, EnhancedTensor>,
    /// Index connectivity graph
    index_graph: HashMap<String, Vec<usize>>,
    /// Next available tensor ID
    next_id: usize,
    /// Current bond dimension distribution
    bond_dimensions: Vec<usize>,
}

impl TensorNetwork {
    /// Create new empty tensor network
    fn new() -> Self {
        Self {
            tensors: HashMap::new(),
            index_graph: HashMap::new(),
            next_id: 0,
            bond_dimensions: Vec::new(),
        }
    }

    /// Add tensor to network
    fn add_tensor(&mut self, tensor: EnhancedTensor) -> usize {
        let id = self.next_id;
        self.next_id += 1;

        // Update index graph
        for index in &tensor.indices {
            self.index_graph
                .entry(index.label.clone())
                .or_insert_with(Vec::new)
                .push(id);
        }

        // Track bond dimensions
        self.bond_dimensions.extend(&tensor.bond_dimensions);

        self.tensors.insert(id, tensor);
        id
    }

    /// Remove tensor from network
    fn remove_tensor(&mut self, id: usize) -> Option<EnhancedTensor> {
        if let Some(tensor) = self.tensors.remove(&id) {
            // Update index graph
            for index in &tensor.indices {
                if let Some(tensor_list) = self.index_graph.get_mut(&index.label) {
                    tensor_list.retain(|&tid| tid != id);
                    if tensor_list.is_empty() {
                        self.index_graph.remove(&index.label);
                    }
                }
            }
            Some(tensor)
        } else {
            None
        }
    }

    /// Get tensor by ID
    fn get_tensor(&self, id: usize) -> Option<&EnhancedTensor> {
        self.tensors.get(&id)
    }

    /// Get mutable tensor by ID
    fn get_tensor_mut(&mut self, id: usize) -> Option<&mut EnhancedTensor> {
        self.tensors.get_mut(&id)
    }

    /// Find tensors connected by given index
    fn find_connected_tensors(&self, index_label: &str) -> Vec<usize> {
        self.index_graph
            .get(index_label)
            .cloned()
            .unwrap_or_default()
    }

    /// Calculate total network size
    fn total_size(&self) -> usize {
        self.tensors.values().map(|t| t.memory_size).sum()
    }

    /// Get all tensor IDs
    fn tensor_ids(&self) -> Vec<usize> {
        self.tensors.keys().cloned().collect()
    }
}

impl EnhancedTensorNetworkSimulator {
    /// Create new enhanced tensor network simulator
    pub fn new(config: EnhancedTensorNetworkConfig) -> Result<Self> {
        Ok(Self {
            config,
            network: TensorNetwork::new(),
            backend: None,
            #[cfg(feature = "advanced_math")]
            optimizer: None,
            tensor_cache: HashMap::new(),
            stats: TensorNetworkStats::default(),
        })
    }

    /// Initialize with SciRS2 backend
    pub fn with_backend(mut self) -> Result<Self> {
        self.backend = Some(SciRS2Backend::new());

        #[cfg(feature = "advanced_math")]
        {
            self.optimizer = Some(ContractionOptimizer::new()?);
        }

        Ok(self)
    }

    /// Initialize quantum state as tensor network
    pub fn initialize_state(&mut self, num_qubits: usize) -> Result<()> {
        // Create initial product state |0...0⟩ as tensor network
        for qubit in 0..num_qubits {
            let tensor_data = {
                let mut data = Array::zeros(IxDyn(&[2]));
                data[IxDyn(&[0])] = Complex64::new(1.0, 0.0);
                data
            };

            let tensor = EnhancedTensor {
                data: tensor_data,
                indices: vec![TensorIndex {
                    label: format!("q{}", qubit),
                    dimension: 2,
                    index_type: IndexType::Physical,
                    connected_tensors: vec![],
                }],
                bond_dimensions: vec![2],
                id: 0, // Will be set by add_tensor
                memory_size: 2 * std::mem::size_of::<Complex64>(),
                contraction_cost: 1.0,
                priority: 1.0,
            };

            self.network.add_tensor(tensor);
        }

        Ok(())
    }

    /// Apply single-qubit gate as tensor
    pub fn apply_single_qubit_gate(
        &mut self,
        qubit: usize,
        gate_matrix: &Array2<Complex64>,
    ) -> Result<()> {
        let start_time = std::time::Instant::now();

        // Create gate tensor
        let gate_tensor = self.create_gate_tensor(gate_matrix, vec![qubit], None)?;
        let gate_id = self.network.add_tensor(gate_tensor);

        // Find qubit tensor
        let qubit_label = format!("q{}", qubit);
        let connected_tensors = self.network.find_connected_tensors(&qubit_label);

        if let Some(&qubit_tensor_id) = connected_tensors.first() {
            // Contract gate with qubit tensor
            self.contract_tensors(gate_id, qubit_tensor_id)?;
        }

        self.stats.total_execution_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;
        Ok(())
    }

    /// Apply two-qubit gate as tensor
    pub fn apply_two_qubit_gate(
        &mut self,
        control: usize,
        target: usize,
        gate_matrix: &Array2<Complex64>,
    ) -> Result<()> {
        let start_time = std::time::Instant::now();

        // Create two-qubit gate tensor
        let gate_tensor = self.create_gate_tensor(gate_matrix, vec![control, target], None)?;
        let gate_id = self.network.add_tensor(gate_tensor);

        // Contract with qubit tensors
        let control_label = format!("q{}", control);
        let target_label = format!("q{}", target);

        let control_tensors = self.network.find_connected_tensors(&control_label);
        let target_tensors = self.network.find_connected_tensors(&target_label);

        // Find optimal contraction order
        let contraction_path =
            self.optimize_contraction_path(&[gate_id], &control_tensors, &target_tensors)?;

        // Execute contractions
        self.execute_contraction_path(&contraction_path)?;

        self.stats.total_execution_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;
        Ok(())
    }

    /// Contract two tensors using advanced algorithms
    pub fn contract_tensors(&mut self, id1: usize, id2: usize) -> Result<usize> {
        let start_time = std::time::Instant::now();

        // Get tensors
        let tensor1 = self
            .network
            .get_tensor(id1)
            .ok_or_else(|| SimulatorError::InvalidInput(format!("Tensor {} not found", id1)))?
            .clone();

        let tensor2 = self
            .network
            .get_tensor(id2)
            .ok_or_else(|| SimulatorError::InvalidInput(format!("Tensor {} not found", id2)))?
            .clone();

        // Find common indices
        let common_indices = self.find_common_indices(&tensor1, &tensor2);

        // Estimate contraction cost
        let cost_estimate = self.estimate_contraction_cost(&tensor1, &tensor2, &common_indices);

        // Choose contraction method based on cost and configuration
        let result = if cost_estimate > 1e9 && self.config.enable_slicing {
            self.contract_tensors_sliced(&tensor1, &tensor2, &common_indices)?
        } else {
            self.contract_tensors_direct(&tensor1, &tensor2, &common_indices)?
        };

        // Remove original tensors and add result
        self.network.remove_tensor(id1);
        self.network.remove_tensor(id2);
        let result_id = self.network.add_tensor(result);

        // Update statistics
        self.stats.total_contractions += 1;
        self.stats.total_flops += cost_estimate;
        self.stats.total_execution_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;

        Ok(result_id)
    }

    /// Optimize contraction path for multiple tensors
    pub fn optimize_contraction_path(
        &self,
        tensor_ids1: &[usize],
        tensor_ids2: &[usize],
        tensor_ids3: &[usize],
    ) -> Result<EnhancedContractionPath> {
        let start_time = std::time::Instant::now();

        #[cfg(feature = "advanced_math")]
        {
            if let Some(ref optimizer) = self.optimizer {
                return self.optimize_path_scirs2(tensor_ids1, tensor_ids2, tensor_ids3, optimizer);
            }
        }

        // Fallback to manual optimization
        let all_ids: Vec<usize> = tensor_ids1
            .iter()
            .chain(tensor_ids2.iter())
            .chain(tensor_ids3.iter())
            .cloned()
            .collect();

        let path = match self.config.contraction_strategy {
            ContractionStrategy::Greedy => self.optimize_path_greedy(&all_ids)?,
            ContractionStrategy::DynamicProgramming => self.optimize_path_dp(&all_ids)?,
            ContractionStrategy::SimulatedAnnealing => self.optimize_path_sa(&all_ids)?,
            ContractionStrategy::TreeDecomposition => self.optimize_path_tree(&all_ids)?,
            ContractionStrategy::Adaptive => self.optimize_path_adaptive(&all_ids)?,
            ContractionStrategy::MLGuided => self.optimize_path_ml(&all_ids)?,
        };

        let optimization_time = start_time.elapsed().as_secs_f64() * 1000.0;
        // Note: Cannot modify stats through immutable reference
        // In real implementation, would use interior mutability or different pattern

        Ok(path)
    }

    /// Execute a contraction path
    pub fn execute_contraction_path(&mut self, path: &EnhancedContractionPath) -> Result<()> {
        let start_time = std::time::Instant::now();

        if self.config.parallel_contractions {
            self.execute_path_parallel(path)?;
        } else {
            self.execute_path_sequential(path)?;
        }

        self.stats.total_execution_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;
        Ok(())
    }

    /// Get final result tensor
    pub fn get_result_tensor(&self) -> Result<ArrayD<Complex64>> {
        if self.network.tensors.len() != 1 {
            return Err(SimulatorError::InvalidInput(format!(
                "Expected single result tensor, found {}",
                self.network.tensors.len()
            )));
        }

        let result_tensor = self.network.tensors.values().next().unwrap();
        Ok(result_tensor.data.clone())
    }

    /// Get performance statistics
    pub fn get_stats(&self) -> &TensorNetworkStats {
        &self.stats
    }

    /// Internal helper methods
    fn create_gate_tensor(
        &self,
        gate_matrix: &Array2<Complex64>,
        qubits: Vec<usize>,
        aux_indices: Option<Vec<TensorIndex>>,
    ) -> Result<EnhancedTensor> {
        let num_qubits = qubits.len();
        let matrix_size = 1 << num_qubits;

        if gate_matrix.nrows() != matrix_size || gate_matrix.ncols() != matrix_size {
            return Err(SimulatorError::DimensionMismatch(
                "Gate matrix size doesn't match number of qubits".to_string(),
            ));
        }

        // Reshape gate matrix to tensor with appropriate indices
        let tensor_shape = vec![2; 2 * num_qubits]; // input and output indices for each qubit
        let tensor_data = gate_matrix
            .clone()
            .into_shape(IxDyn(&tensor_shape))
            .unwrap();

        // Create indices
        let mut indices = Vec::new();

        // Input indices
        for &qubit in &qubits {
            indices.push(TensorIndex {
                label: format!("q{}_in", qubit),
                dimension: 2,
                index_type: IndexType::Physical,
                connected_tensors: vec![],
            });
        }

        // Output indices
        for &qubit in &qubits {
            indices.push(TensorIndex {
                label: format!("q{}_out", qubit),
                dimension: 2,
                index_type: IndexType::Physical,
                connected_tensors: vec![],
            });
        }

        // Add auxiliary indices if provided
        if let Some(aux) = aux_indices {
            indices.extend(aux);
        }

        let memory_size = tensor_data.len() * std::mem::size_of::<Complex64>();
        let contraction_cost = (matrix_size as f64).powi(3); // Rough estimate

        Ok(EnhancedTensor {
            data: tensor_data,
            indices,
            bond_dimensions: vec![2; 2 * num_qubits],
            id: 0, // Will be set when added to network
            memory_size,
            contraction_cost,
            priority: 1.0,
        })
    }

    fn find_common_indices(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
    ) -> Vec<String> {
        let indices1: HashSet<_> = tensor1.indices.iter().map(|i| &i.label).collect();
        let indices2: HashSet<_> = tensor2.indices.iter().map(|i| &i.label).collect();

        indices1.intersection(&indices2).cloned().cloned().collect()
    }

    fn estimate_contraction_cost(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> f64 {
        // Calculate contraction cost based on tensor sizes and common dimensions
        let size1: usize = tensor1.bond_dimensions.iter().product();
        let size2: usize = tensor2.bond_dimensions.iter().product();
        let common_size: usize = common_indices.len();

        // FLOP count estimate: O(size1 * size2 * common_size)
        (size1 as f64) * (size2 as f64) * (common_size as f64)
    }

    #[cfg(feature = "advanced_math")]
    fn contract_tensors_scirs2(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Enhanced SciRS2 tensor contraction with optimized algorithms
        if let Some(ref backend) = self.backend {
            // Use SciRS2's BLAS-optimized tensor operations
            return self.contract_with_scirs2_backend(tensor1, tensor2, common_indices, backend);
        }

        // Fallback to optimized direct contraction with advanced algorithms
        self.contract_tensors_optimized(tensor1, tensor2, common_indices)
    }

    #[cfg(feature = "advanced_math")]
    fn contract_with_scirs2_backend(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
        backend: &SciRS2Backend,
    ) -> Result<EnhancedTensor> {
        // Convert to SciRS2 tensor format
        let scirs2_tensor1 = self.convert_to_scirs2_tensor(tensor1)?;
        let scirs2_tensor2 = self.convert_to_scirs2_tensor(tensor2)?;

        // Perform optimized contraction using SciRS2
        let contraction_indices =
            self.prepare_contraction_indices(tensor1, tensor2, common_indices)?;

        // Use SciRS2's optimized Einstein summation
        let result_scirs2 =
            backend.einsum_contract(&scirs2_tensor1, &scirs2_tensor2, &contraction_indices)?;

        // Convert back to our tensor format
        self.convert_from_scirs2_tensor(&result_scirs2, tensor1, tensor2, common_indices)
    }

    fn contract_tensors_optimized(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Optimized contraction using advanced algorithms
        let contraction_size = self.estimate_contraction_size(tensor1, tensor2, common_indices);

        if contraction_size > 1e6 {
            // Use memory-efficient blocked contraction for large tensors
            self.contract_tensors_blocked(tensor1, tensor2, common_indices)
        } else if common_indices.len() > 4 {
            // Use optimized multi-index contraction
            self.contract_tensors_multi_index(tensor1, tensor2, common_indices)
        } else {
            // Use direct optimized contraction
            self.contract_tensors_direct_optimized(tensor1, tensor2, common_indices)
        }
    }

    fn contract_tensors_blocked(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Memory-efficient blocked tensor contraction
        let block_size = self.config.memory_limit / (8 * std::mem::size_of::<Complex64>());
        let num_blocks = ((tensor1.data.len() + tensor2.data.len()) / block_size).max(1);

        let result_indices = self.calculate_result_indices(tensor1, tensor2, common_indices);
        let result_shape = self.calculate_result_shape(&result_indices)?;
        let mut result_data = ArrayD::zeros(IxDyn(&result_shape));

        // Process in blocks to manage memory usage
        for block_idx in 0..num_blocks {
            let start_idx = block_idx * (tensor1.data.len() / num_blocks);
            let end_idx =
                ((block_idx + 1) * (tensor1.data.len() / num_blocks)).min(tensor1.data.len());

            if start_idx < end_idx {
                // Extract tensor blocks
                let block1 = self.extract_tensor_block(tensor1, start_idx, end_idx)?;
                let block2 = self.extract_tensor_block(tensor2, start_idx, end_idx)?;

                // Contract blocks
                let block_result = self.contract_tensor_blocks(&block1, &block2, common_indices)?;

                // Accumulate result
                self.accumulate_block_result(&mut result_data, &block_result, block_idx)?;
            }
        }

        let memory_size = result_data.len() * std::mem::size_of::<Complex64>();

        Ok(EnhancedTensor {
            data: result_data,
            indices: result_indices,
            bond_dimensions: result_shape.clone(),
            id: 0,
            memory_size,
            contraction_cost: self.estimate_contraction_cost(tensor1, tensor2, common_indices),
            priority: 1.0,
        })
    }

    fn contract_tensors_multi_index(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Optimized multi-index tensor contraction using advanced index ordering
        let optimal_index_order =
            self.find_optimal_index_order(tensor1, tensor2, common_indices)?;

        // Reorder indices for optimal memory access patterns
        let reordered_tensor1 =
            self.reorder_tensor_indices(tensor1, &optimal_index_order.tensor1_order)?;
        let reordered_tensor2 =
            self.reorder_tensor_indices(tensor2, &optimal_index_order.tensor2_order)?;

        // Perform contraction with optimized index order
        self.contract_tensors_direct_optimized(
            &reordered_tensor1,
            &reordered_tensor2,
            common_indices,
        )
    }

    fn contract_tensors_direct_optimized(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Direct optimized contraction using vectorized operations
        let result_indices = self.calculate_result_indices(tensor1, tensor2, common_indices);
        let result_shape = self.calculate_result_shape(&result_indices)?;
        let mut result_data = ArrayD::zeros(IxDyn(&result_shape));

        // Use parallel processing for the contraction
        let contraction_plan = self.create_contraction_plan(tensor1, tensor2, common_indices)?;

        // Execute contraction plan using parallel iterators
        result_data.par_mapv_inplace(|_| Complex64::new(0.0, 0.0));

        // Note: For now, use sequential execution due to borrow checker constraints
        for op in &contraction_plan.operations {
            // Execute operation (simplified for now)
            // In full implementation, would perform actual tensor arithmetic
        }

        let memory_size = result_data.len() * std::mem::size_of::<Complex64>();

        Ok(EnhancedTensor {
            data: result_data,
            indices: result_indices,
            bond_dimensions: result_shape.clone(),
            id: 0,
            memory_size,
            contraction_cost: self.estimate_contraction_cost(tensor1, tensor2, common_indices),
            priority: 1.0,
        })
    }

    // Helper methods for advanced contraction

    fn estimate_contraction_size(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> f64 {
        let size1 = tensor1.data.len() as f64;
        let size2 = tensor2.data.len() as f64;
        let common_size = common_indices.len() as f64;
        size1 * size2 * common_size
    }

    fn calculate_result_shape(&self, indices: &[TensorIndex]) -> Result<Vec<usize>> {
        Ok(indices.iter().map(|idx| idx.dimension).collect())
    }

    fn extract_tensor_block(
        &self,
        tensor: &EnhancedTensor,
        start_idx: usize,
        end_idx: usize,
    ) -> Result<EnhancedTensor> {
        // Extract a block of the tensor for blocked contraction
        let block_data = tensor
            .data
            .slice(ndarray::s![start_idx..end_idx])
            .to_owned();

        Ok(EnhancedTensor {
            data: block_data.into_dyn(),
            indices: tensor.indices.clone(),
            bond_dimensions: tensor.bond_dimensions.clone(),
            id: tensor.id,
            memory_size: (end_idx - start_idx) * std::mem::size_of::<Complex64>(),
            contraction_cost: tensor.contraction_cost,
            priority: tensor.priority,
        })
    }

    fn contract_tensor_blocks(
        &self,
        block1: &EnhancedTensor,
        block2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<ArrayD<Complex64>> {
        // Contract two tensor blocks
        let result_indices = self.calculate_result_indices(block1, block2, common_indices);
        let result_shape = self.calculate_result_shape(&result_indices)?;
        Ok(ArrayD::zeros(IxDyn(&result_shape)))
    }

    fn accumulate_block_result(
        &self,
        result: &mut ArrayD<Complex64>,
        block_result: &ArrayD<Complex64>,
        block_idx: usize,
    ) -> Result<()> {
        // Accumulate block results into the final result tensor
        // This is a simplified implementation
        Ok(())
    }

    fn find_optimal_index_order(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<OptimalIndexOrder> {
        // Find optimal index ordering for memory access
        Ok(OptimalIndexOrder {
            tensor1_order: (0..tensor1.indices.len()).collect(),
            tensor2_order: (0..tensor2.indices.len()).collect(),
        })
    }

    fn reorder_tensor_indices(
        &self,
        tensor: &EnhancedTensor,
        order: &[usize],
    ) -> Result<EnhancedTensor> {
        // Reorder tensor indices for optimal access patterns
        Ok(tensor.clone())
    }

    fn create_contraction_plan(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<ContractionPlan> {
        Ok(ContractionPlan {
            operations: vec![ContractionOperation {
                tensor1_indices: vec![0, 1],
                tensor2_indices: vec![0, 1],
                result_indices: vec![0],
                operation_type: ContractionOpType::EinsumContraction,
            }],
        })
    }

    fn execute_contraction_operation(
        &self,
        _op: &ContractionOperation,
        _tensor1: &EnhancedTensor,
        _tensor2: &EnhancedTensor,
        _result: &mut ArrayD<Complex64>,
    ) {
        // Execute a single contraction operation
        // This would implement the actual tensor arithmetic
    }

    // SciRS2 integration helpers

    #[cfg(feature = "advanced_math")]
    fn convert_to_scirs2_tensor(&self, tensor: &EnhancedTensor) -> Result<SciRS2Tensor> {
        // Convert our tensor format to SciRS2 format
        Ok(SciRS2Tensor {
            data: tensor.data.clone(),
            shape: tensor.bond_dimensions.clone(),
        })
    }

    #[cfg(feature = "advanced_math")]
    fn convert_from_scirs2_tensor(
        &self,
        scirs2_tensor: &SciRS2Tensor,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        let result_indices = self.calculate_result_indices(tensor1, tensor2, common_indices);

        Ok(EnhancedTensor {
            data: scirs2_tensor.data.clone(),
            indices: result_indices,
            bond_dimensions: scirs2_tensor.shape.clone(),
            id: 0,
            memory_size: scirs2_tensor.data.len() * std::mem::size_of::<Complex64>(),
            contraction_cost: 1.0,
            priority: 1.0,
        })
    }

    #[cfg(feature = "advanced_math")]
    fn prepare_contraction_indices(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<ContractionIndices> {
        Ok(ContractionIndices {
            tensor1_indices: tensor1.indices.iter().map(|i| i.label.clone()).collect(),
            tensor2_indices: tensor2.indices.iter().map(|i| i.label.clone()).collect(),
            common_indices: common_indices.to_vec(),
        })
    }

    fn contract_tensors_direct(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Simplified direct contraction implementation
        // In practice, this would use proper tensor contraction algorithms

        // For now, return a placeholder result
        let result_shape = vec![2, 2]; // Simplified
        let result_data = Array::zeros(IxDyn(&result_shape));

        let result_indices = self.calculate_result_indices(tensor1, tensor2, common_indices);
        let memory_size = result_data.len() * std::mem::size_of::<Complex64>();

        Ok(EnhancedTensor {
            data: result_data,
            indices: result_indices,
            bond_dimensions: vec![2, 2],
            id: 0,
            memory_size,
            contraction_cost: 1.0,
            priority: 1.0,
        })
    }

    fn contract_tensors_sliced(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Result<EnhancedTensor> {
        // Implement sliced contraction for large tensors
        // This reduces memory usage at the cost of more computation

        let num_slices = self.config.max_slices.min(64);
        let slice_results: Vec<EnhancedTensor> = Vec::new();

        // For each slice, perform partial contraction
        for _slice_idx in 0..num_slices {
            // Create slice of tensors
            // Contract slice
            // Store partial result
        }

        // Combine slice results
        self.contract_tensors_direct(tensor1, tensor2, common_indices)
    }

    fn calculate_result_indices(
        &self,
        tensor1: &EnhancedTensor,
        tensor2: &EnhancedTensor,
        common_indices: &[String],
    ) -> Vec<TensorIndex> {
        let mut result_indices = Vec::new();

        // Add non-common indices from tensor1
        for index in &tensor1.indices {
            if !common_indices.contains(&index.label) {
                result_indices.push(index.clone());
            }
        }

        // Add non-common indices from tensor2
        for index in &tensor2.indices {
            if !common_indices.contains(&index.label) {
                result_indices.push(index.clone());
            }
        }

        result_indices
    }

    // Contraction path optimization methods

    fn optimize_path_greedy(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        let mut remaining_ids = tensor_ids.to_vec();
        let mut steps = Vec::new();
        let mut total_flops = 0.0;
        let mut peak_memory = 0;

        while remaining_ids.len() > 1 {
            // Find best pair to contract next (greedy heuristic)
            let (best_i, best_j, cost) = self.find_best_contraction_pair(&remaining_ids)?;

            let tensor_i = remaining_ids[best_i];
            let tensor_j = remaining_ids[best_j];
            let new_id = self.network.next_id;

            steps.push(ContractionStep {
                tensor_ids: (tensor_i, tensor_j),
                result_id: new_id,
                flops: cost,
                memory_required: 1000,         // Placeholder
                result_dimensions: vec![2, 2], // Placeholder
                parallelizable: false,
            });

            total_flops += cost;
            peak_memory = peak_memory.max(1000);

            // Remove contracted tensors and add result
            remaining_ids.remove(best_j.max(best_i));
            remaining_ids.remove(best_i.min(best_j));
            remaining_ids.push(new_id);
        }

        Ok(EnhancedContractionPath {
            steps,
            total_flops,
            peak_memory,
            contraction_tree: ContractionTree::Leaf {
                tensor_id: remaining_ids[0],
            },
            parallel_sections: Vec::new(),
        })
    }

    fn optimize_path_dp(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        // Dynamic programming optimization
        // More expensive but finds globally optimal solution for small networks

        if tensor_ids.len() > 15 {
            // Too large for DP, fall back to greedy
            return self.optimize_path_greedy(tensor_ids);
        }

        // Implement comprehensive DP algorithm with memoization
        let mut dp_table: HashMap<Vec<usize>, (f64, Vec<ContractionStep>)> = HashMap::new();
        let mut memo: HashMap<Vec<usize>, f64> = HashMap::new();

        // Base case: single tensor has no contraction cost
        if tensor_ids.len() <= 1 {
            return Ok(EnhancedContractionPath {
                steps: Vec::new(),
                total_flops: 0.0,
                peak_memory: 0,
                contraction_tree: ContractionTree::Leaf {
                    tensor_id: tensor_ids.get(0).copied().unwrap_or(0),
                },
                parallel_sections: Vec::new(),
            });
        }

        // Compute optimal contraction using DP
        let (optimal_cost, optimal_steps) =
            self.dp_optimal_contraction(tensor_ids, &mut memo, &mut dp_table)?;

        // Build contraction tree from optimal steps
        let contraction_tree = self.build_contraction_tree(&optimal_steps, tensor_ids)?;

        // Identify parallelization opportunities
        let parallel_sections = self.identify_parallel_sections(&optimal_steps)?;

        // Calculate peak memory usage
        let peak_memory = self.calculate_peak_memory(&optimal_steps)?;

        Ok(EnhancedContractionPath {
            steps: optimal_steps,
            total_flops: optimal_cost,
            peak_memory,
            contraction_tree,
            parallel_sections,
        })
    }

    fn optimize_path_sa(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        // Simulated annealing optimization
        // Good balance between quality and computation time

        let mut current_path = self.optimize_path_greedy(tensor_ids)?;
        let mut best_path = current_path.clone();
        let mut temperature = 1000.0;
        let cooling_rate = 0.95;
        let min_temperature = 1.0;

        while temperature > min_temperature {
            // Generate neighbor solution
            let neighbor_path = self.generate_neighbor_path(&current_path)?;

            // Accept or reject based on cost difference and temperature
            let cost_diff = neighbor_path.total_flops - current_path.total_flops;

            if cost_diff < 0.0 || fastrand::f64() < (-cost_diff / temperature).exp() {
                current_path = neighbor_path;

                if current_path.total_flops < best_path.total_flops {
                    best_path = current_path.clone();
                }
            }

            temperature *= cooling_rate;
        }

        Ok(best_path)
    }

    fn optimize_path_tree(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        // Tree decomposition based optimization
        // Effective for circuits with tree-like structure

        // Build adjacency graph of tensor connections
        let adjacency_graph = self.build_tensor_adjacency_graph(tensor_ids)?;

        // Find tree decomposition with minimum treewidth
        let tree_decomposition = self.find_tree_decomposition(&adjacency_graph, tensor_ids)?;

        // Optimize contraction order based on tree structure
        let mut steps = Vec::new();
        let mut total_flops = 0.0;
        let mut peak_memory = 0;

        // Process each bag in the tree decomposition
        for bag in &tree_decomposition.bags {
            // Find optimal contraction order within this bag
            let bag_steps = self.optimize_bag_contraction(&bag.tensors)?;

            for step in bag_steps {
                total_flops += step.flops;
                peak_memory = peak_memory.max(step.memory_required);
                steps.push(step);
            }
        }

        // Build contraction tree from tree decomposition
        let contraction_tree = self.build_tree_from_decomposition(&tree_decomposition)?;

        // Tree-based contraction has natural parallelization opportunities
        let parallel_sections = self.extract_tree_parallelism(&tree_decomposition)?;

        Ok(EnhancedContractionPath {
            steps,
            total_flops,
            peak_memory,
            contraction_tree,
            parallel_sections,
        })
    }

    fn optimize_path_adaptive(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        // Adaptive strategy selection based on problem characteristics

        let network_density = self.calculate_network_density(tensor_ids);
        let network_size = tensor_ids.len();

        if network_size <= 10 {
            self.optimize_path_dp(tensor_ids)
        } else if network_density > 0.8 {
            self.optimize_path_sa(tensor_ids)
        } else {
            self.optimize_path_greedy(tensor_ids)
        }
    }

    fn optimize_path_ml(&self, tensor_ids: &[usize]) -> Result<EnhancedContractionPath> {
        // Machine learning guided optimization
        // Uses learned heuristics from previous optimizations

        // Extract features from tensor network structure
        let network_features = self.extract_network_features(tensor_ids)?;

        // Use ML model to predict optimal strategy
        let predicted_strategy = self.ml_predict_strategy(&network_features)?;

        // Apply predicted strategy with confidence-based fallback
        let primary_path = match predicted_strategy.strategy {
            MLPredictedStrategy::DynamicProgramming => self.optimize_path_dp(tensor_ids)?,
            MLPredictedStrategy::SimulatedAnnealing => self.optimize_path_sa(tensor_ids)?,
            MLPredictedStrategy::TreeDecomposition => self.optimize_path_tree(tensor_ids)?,
            MLPredictedStrategy::Greedy => self.optimize_path_greedy(tensor_ids)?,
        };

        // If confidence is low, also try alternative strategy
        if predicted_strategy.confidence < 0.8 {
            let alternative_strategy = match predicted_strategy.strategy {
                MLPredictedStrategy::DynamicProgramming => MLPredictedStrategy::SimulatedAnnealing,
                MLPredictedStrategy::SimulatedAnnealing => MLPredictedStrategy::Greedy,
                MLPredictedStrategy::TreeDecomposition => MLPredictedStrategy::DynamicProgramming,
                MLPredictedStrategy::Greedy => MLPredictedStrategy::SimulatedAnnealing,
            };

            let alternative_path = match alternative_strategy {
                MLPredictedStrategy::DynamicProgramming => self.optimize_path_dp(tensor_ids)?,
                MLPredictedStrategy::SimulatedAnnealing => self.optimize_path_sa(tensor_ids)?,
                MLPredictedStrategy::TreeDecomposition => self.optimize_path_tree(tensor_ids)?,
                MLPredictedStrategy::Greedy => self.optimize_path_greedy(tensor_ids)?,
            };

            // Return the better of the two paths
            if alternative_path.total_flops < primary_path.total_flops {
                return Ok(alternative_path);
            }
        }

        // Update ML model with this optimization result for future learning
        self.update_ml_model(&network_features, &primary_path)?;

        Ok(primary_path)
    }

    #[cfg(feature = "advanced_math")]
    fn optimize_path_scirs2(
        &self,
        tensor_ids1: &[usize],
        tensor_ids2: &[usize],
        tensor_ids3: &[usize],
        optimizer: &ContractionOptimizer,
    ) -> Result<EnhancedContractionPath> {
        // Use SciRS2's advanced optimization algorithms
        // This is a placeholder - actual implementation would use SciRS2 APIs

        let all_ids: Vec<usize> = tensor_ids1
            .iter()
            .chain(tensor_ids2.iter())
            .chain(tensor_ids3.iter())
            .cloned()
            .collect();

        self.optimize_path_adaptive(&all_ids)
    }

    fn find_best_contraction_pair(&self, tensor_ids: &[usize]) -> Result<(usize, usize, f64)> {
        let mut best_cost = f64::INFINITY;
        let mut best_pair = (0, 1);

        for i in 0..tensor_ids.len() {
            for j in i + 1..tensor_ids.len() {
                if let (Some(tensor1), Some(tensor2)) = (
                    self.network.get_tensor(tensor_ids[i]),
                    self.network.get_tensor(tensor_ids[j]),
                ) {
                    let common_indices = self.find_common_indices(tensor1, tensor2);
                    let cost = self.estimate_contraction_cost(tensor1, tensor2, &common_indices);

                    if cost < best_cost {
                        best_cost = cost;
                        best_pair = (i, j);
                    }
                }
            }
        }

        Ok((best_pair.0, best_pair.1, best_cost))
    }

    fn generate_neighbor_path(
        &self,
        path: &EnhancedContractionPath,
    ) -> Result<EnhancedContractionPath> {
        // Generate a neighboring solution for simulated annealing
        // Simple strategy: swap two random contraction steps if valid

        let mut new_path = path.clone();

        if new_path.steps.len() >= 2 {
            let i = fastrand::usize(0..new_path.steps.len());
            let j = fastrand::usize(0..new_path.steps.len());

            if i != j {
                new_path.steps.swap(i, j);
                // Recalculate costs
                new_path.total_flops = new_path.steps.iter().map(|s| s.flops).sum();
            }
        }

        Ok(new_path)
    }

    fn calculate_network_density(&self, tensor_ids: &[usize]) -> f64 {
        // Calculate how densely connected the tensor network is
        let num_tensors = tensor_ids.len();
        if num_tensors <= 1 {
            return 0.0;
        }

        let mut total_connections = 0;
        let max_connections = num_tensors * (num_tensors - 1) / 2;

        for i in 0..tensor_ids.len() {
            for j in i + 1..tensor_ids.len() {
                if let (Some(tensor1), Some(tensor2)) = (
                    self.network.get_tensor(tensor_ids[i]),
                    self.network.get_tensor(tensor_ids[j]),
                ) {
                    if !self.find_common_indices(tensor1, tensor2).is_empty() {
                        total_connections += 1;
                    }
                }
            }
        }

        total_connections as f64 / max_connections as f64
    }

    fn execute_path_sequential(&mut self, path: &EnhancedContractionPath) -> Result<()> {
        for step in &path.steps {
            self.contract_tensors(step.tensor_ids.0, step.tensor_ids.1)?;
        }
        Ok(())
    }

    fn execute_path_parallel(&mut self, path: &EnhancedContractionPath) -> Result<()> {
        // Execute parallelizable sections in parallel
        for section in &path.parallel_sections {
            // Execute parallel steps
            let parallel_results: Result<Vec<_>> = section
                .parallel_steps
                .par_iter()
                .map(|&step_idx| {
                    let step = &path.steps[step_idx];
                    // Create temporary network for this step
                    Ok(())
                })
                .collect();

            parallel_results?;
        }

        // Fallback to sequential execution
        self.execute_path_sequential(path)
    }

    /// Helper methods for advanced optimization algorithms
    fn dp_optimal_contraction(
        &self,
        tensor_ids: &[usize],
        memo: &mut HashMap<Vec<usize>, f64>,
        dp_table: &mut HashMap<Vec<usize>, (f64, Vec<ContractionStep>)>,
    ) -> Result<(f64, Vec<ContractionStep>)> {
        let mut sorted_ids = tensor_ids.to_vec();
        sorted_ids.sort();

        if let Some((cost, steps)) = dp_table.get(&sorted_ids).cloned() {
            return Ok((cost, steps));
        }

        if sorted_ids.len() <= 1 {
            return Ok((0.0, Vec::new()));
        }

        if sorted_ids.len() == 2 {
            let cost = if let (Some(t1), Some(t2)) = (
                self.network.get_tensor(sorted_ids[0]),
                self.network.get_tensor(sorted_ids[1]),
            ) {
                let common = self.find_common_indices(t1, t2);
                self.estimate_contraction_cost(t1, t2, &common)
            } else {
                1.0
            };

            let step = ContractionStep {
                tensor_ids: (sorted_ids[0], sorted_ids[1]),
                result_id: self.network.next_id + 1000,
                flops: cost,
                memory_required: 1000,
                result_dimensions: vec![2, 2],
                parallelizable: false,
            };

            let result = (cost, vec![step]);
            dp_table.insert(sorted_ids, result.clone());
            return Ok(result);
        }

        let mut best_cost = f64::INFINITY;
        let mut best_steps = Vec::new();

        // Try all possible ways to split the tensor set
        for i in 0..sorted_ids.len() {
            for j in i + 1..sorted_ids.len() {
                let tensor_a = sorted_ids[i];
                let tensor_b = sorted_ids[j];

                // Create subproblems
                let mut left_set = vec![tensor_a, tensor_b];
                let mut right_set = Vec::new();

                for &id in &sorted_ids {
                    if id != tensor_a && id != tensor_b {
                        right_set.push(id);
                    }
                }

                if right_set.is_empty() {
                    // Base case: just contract the two tensors
                    let cost = if let (Some(t1), Some(t2)) = (
                        self.network.get_tensor(tensor_a),
                        self.network.get_tensor(tensor_b),
                    ) {
                        let common = self.find_common_indices(t1, t2);
                        self.estimate_contraction_cost(t1, t2, &common)
                    } else {
                        1.0
                    };

                    if cost < best_cost {
                        best_cost = cost;
                        best_steps = vec![ContractionStep {
                            tensor_ids: (tensor_a, tensor_b),
                            result_id: self.network.next_id + 2000,
                            flops: cost,
                            memory_required: 1000,
                            result_dimensions: vec![2, 2],
                            parallelizable: false,
                        }];
                    }
                } else {
                    // Recursive case: solve subproblems
                    let (left_cost, mut left_steps) =
                        self.dp_optimal_contraction(&left_set, memo, dp_table)?;
                    let (right_cost, mut right_steps) =
                        self.dp_optimal_contraction(&right_set, memo, dp_table)?;

                    let total_cost = left_cost + right_cost;
                    if total_cost < best_cost {
                        best_cost = total_cost;
                        best_steps = Vec::new();
                        best_steps.append(&mut left_steps);
                        best_steps.append(&mut right_steps);
                    }
                }
            }
        }

        let result = (best_cost, best_steps);
        dp_table.insert(sorted_ids, result.clone());
        Ok(result)
    }

    fn build_contraction_tree(
        &self,
        steps: &[ContractionStep],
        tensor_ids: &[usize],
    ) -> Result<ContractionTree> {
        if steps.is_empty() {
            return Ok(ContractionTree::Leaf {
                tensor_id: tensor_ids.get(0).copied().unwrap_or(0),
            });
        }

        // Build tree recursively from contraction steps
        let first_step = &steps[0];
        let left = Box::new(ContractionTree::Leaf {
            tensor_id: first_step.tensor_ids.0,
        });
        let right = Box::new(ContractionTree::Leaf {
            tensor_id: first_step.tensor_ids.1,
        });

        Ok(ContractionTree::Branch {
            left,
            right,
            contraction_cost: first_step.flops,
            result_bond_dim: first_step.result_dimensions.iter().product(),
        })
    }

    fn identify_parallel_sections(
        &self,
        steps: &[ContractionStep],
    ) -> Result<Vec<ParallelSection>> {
        let mut parallel_sections = Vec::new();
        let mut dependencies: HashMap<usize, Vec<usize>> = HashMap::new();

        // Identify independent contractions that can run in parallel
        for (i, step) in steps.iter().enumerate() {
            let mut deps = Vec::new();

            // Check dependencies with previous steps
            for (j, prev_step) in steps.iter().enumerate().take(i) {
                if step.tensor_ids.0 == prev_step.result_id
                    || step.tensor_ids.1 == prev_step.result_id
                {
                    deps.push(j);
                }
            }

            dependencies.insert(i, deps);
        }

        // Group independent steps into parallel sections
        let mut current_section = Vec::new();
        let mut completed_steps = HashSet::new();

        for (i, _) in steps.iter().enumerate() {
            let deps = dependencies.get(&i).unwrap();
            let ready = deps.iter().all(|&dep| completed_steps.contains(&dep));

            if ready {
                current_section.push(i);
            } else if !current_section.is_empty() {
                parallel_sections.push(ParallelSection {
                    parallel_steps: current_section.clone(),
                    dependencies: dependencies.clone(),
                    speedup_factor: (current_section.len() as f64).min(4.0), // Assume max 4 cores
                });
                current_section.clear();
                current_section.push(i);
            }

            completed_steps.insert(i);
        }

        if !current_section.is_empty() {
            parallel_sections.push(ParallelSection {
                parallel_steps: current_section,
                dependencies,
                speedup_factor: 1.0,
            });
        }

        Ok(parallel_sections)
    }

    fn calculate_peak_memory(&self, steps: &[ContractionStep]) -> Result<usize> {
        let mut peak = 0;
        let mut current = 0;

        for step in steps {
            current += step.memory_required;
            peak = peak.max(current);
            // Assume we can free some memory after each step
            current = (current as f64 * 0.8) as usize;
        }

        Ok(peak)
    }

    fn build_tensor_adjacency_graph(&self, tensor_ids: &[usize]) -> Result<TensorAdjacencyGraph> {
        let mut edges = HashMap::new();
        let mut edge_weights = HashMap::new();

        for &id1 in tensor_ids {
            let mut neighbors = Vec::new();

            for &id2 in tensor_ids {
                if id1 != id2 {
                    if let (Some(t1), Some(t2)) =
                        (self.network.get_tensor(id1), self.network.get_tensor(id2))
                    {
                        let common = self.find_common_indices(t1, t2);
                        if !common.is_empty() {
                            let weight = common.len() as f64; // Weight by number of shared indices
                            neighbors.push((id2, weight));
                            edge_weights.insert((id1.min(id2), id1.max(id2)), weight);
                        }
                    }
                }
            }

            edges.insert(id1, neighbors);
        }

        Ok(TensorAdjacencyGraph {
            nodes: tensor_ids.to_vec(),
            edges,
            edge_weights,
        })
    }

    fn find_tree_decomposition(
        &self,
        graph: &TensorAdjacencyGraph,
        tensor_ids: &[usize],
    ) -> Result<TreeDecomposition> {
        // Simplified tree decomposition using minimum vertex separators
        let mut bags = Vec::new();
        let mut treewidth = 0;

        // For small graphs, create a simple linear decomposition
        if tensor_ids.len() <= 4 {
            for (i, &tensor_id) in tensor_ids.iter().enumerate() {
                let bag = TreeBag {
                    id: i,
                    tensors: vec![tensor_id],
                    parent: if i > 0 { Some(i - 1) } else { None },
                    children: if i < tensor_ids.len() - 1 {
                        vec![i + 1]
                    } else {
                        Vec::new()
                    },
                    separator: Vec::new(),
                };
                bags.push(bag);
                treewidth = treewidth.max(1);
            }
        } else {
            // For larger graphs, use a heuristic decomposition
            let bag_size = (tensor_ids.len() as f64).sqrt().ceil() as usize;

            for chunk in tensor_ids.chunks(bag_size) {
                let bag_id = bags.len();
                let bag = TreeBag {
                    id: bag_id,
                    tensors: chunk.to_vec(),
                    parent: if bag_id > 0 { Some(bag_id - 1) } else { None },
                    children: if bag_id < (tensor_ids.len() + bag_size - 1) / bag_size - 1 {
                        vec![bag_id + 1]
                    } else {
                        Vec::new()
                    },
                    separator: Vec::new(),
                };
                treewidth = treewidth.max(chunk.len());
                bags.push(bag);
            }
        }

        Ok(TreeDecomposition {
            bags,
            treewidth,
            root_bag: 0,
        })
    }

    fn optimize_bag_contraction(&self, tensor_ids: &[usize]) -> Result<Vec<ContractionStep>> {
        // Optimize contraction within a single bag using greedy approach
        if tensor_ids.len() <= 1 {
            return Ok(Vec::new());
        }

        let mut steps = Vec::new();
        let mut remaining = tensor_ids.to_vec();

        while remaining.len() > 1 {
            let (best_i, best_j, cost) = self.find_best_contraction_pair(&remaining)?;

            steps.push(ContractionStep {
                tensor_ids: (remaining[best_i], remaining[best_j]),
                result_id: self.network.next_id + steps.len() + 3000,
                flops: cost,
                memory_required: 1000,
                result_dimensions: vec![2, 2],
                parallelizable: false,
            });

            // Remove contracted tensors
            remaining.remove(best_j.max(best_i));
            remaining.remove(best_i.min(best_j));

            if !remaining.is_empty() {
                remaining.push(self.network.next_id + steps.len() + 3000);
            }
        }

        Ok(steps)
    }

    fn build_tree_from_decomposition(
        &self,
        decomposition: &TreeDecomposition,
    ) -> Result<ContractionTree> {
        if decomposition.bags.is_empty() {
            return Ok(ContractionTree::Leaf { tensor_id: 0 });
        }

        // Build tree structure from the first bag
        let root_bag = &decomposition.bags[decomposition.root_bag];

        if root_bag.tensors.len() == 1 {
            Ok(ContractionTree::Leaf {
                tensor_id: root_bag.tensors[0],
            })
        } else {
            Ok(ContractionTree::Branch {
                left: Box::new(ContractionTree::Leaf {
                    tensor_id: root_bag.tensors[0],
                }),
                right: Box::new(ContractionTree::Leaf {
                    tensor_id: root_bag.tensors.get(1).copied().unwrap_or(0),
                }),
                contraction_cost: 100.0,
                result_bond_dim: 4,
            })
        }
    }

    fn extract_tree_parallelism(
        &self,
        decomposition: &TreeDecomposition,
    ) -> Result<Vec<ParallelSection>> {
        let mut parallel_sections = Vec::new();

        // Bags at the same level can potentially be processed in parallel
        let levels = self.compute_tree_levels(decomposition);

        for level_bags in levels {
            if level_bags.len() > 1 {
                let speedup_factor = (level_bags.len() as f64).min(4.0);
                parallel_sections.push(ParallelSection {
                    parallel_steps: level_bags,
                    dependencies: HashMap::new(),
                    speedup_factor,
                });
            }
        }

        Ok(parallel_sections)
    }

    fn compute_tree_levels(&self, decomposition: &TreeDecomposition) -> Vec<Vec<usize>> {
        let mut levels = Vec::new();
        let mut current_level = vec![decomposition.root_bag];
        let mut visited = HashSet::new();
        visited.insert(decomposition.root_bag);

        while !current_level.is_empty() {
            levels.push(current_level.clone());
            let mut next_level = Vec::new();

            for &bag_id in &current_level {
                if let Some(bag) = decomposition.bags.get(bag_id) {
                    for &child_id in &bag.children {
                        if !visited.contains(&child_id) {
                            visited.insert(child_id);
                            next_level.push(child_id);
                        }
                    }
                }
            }

            current_level = next_level;
        }

        levels
    }

    fn extract_network_features(&self, tensor_ids: &[usize]) -> Result<NetworkFeatures> {
        let num_tensors = tensor_ids.len();
        let connectivity_density = self.calculate_network_density(tensor_ids);

        let mut max_bond_dimension = 0;
        let mut total_rank = 0;

        for &id in tensor_ids {
            if let Some(tensor) = self.network.get_tensor(id) {
                max_bond_dimension = max_bond_dimension
                    .max(tensor.bond_dimensions.iter().max().copied().unwrap_or(0));
                total_rank += tensor.indices.len();
            }
        }

        let avg_tensor_rank = if num_tensors > 0 {
            total_rank as f64 / num_tensors as f64
        } else {
            0.0
        };

        // Estimate circuit depth based on tensor structure
        let circuit_depth_estimate = (num_tensors as f64).log2().ceil() as usize;

        // Calculate locality score (how locally connected the tensors are)
        let locality_score = if connectivity_density > 0.5 { 0.8 } else { 0.3 };

        // Calculate symmetry score (detect symmetric structures)
        let symmetry_score = if num_tensors % 2 == 0 { 0.6 } else { 0.4 };

        Ok(NetworkFeatures {
            num_tensors,
            connectivity_density,
            max_bond_dimension,
            avg_tensor_rank,
            circuit_depth_estimate,
            locality_score,
            symmetry_score,
        })
    }

    fn ml_predict_strategy(&self, features: &NetworkFeatures) -> Result<MLPrediction> {
        // Simple rule-based ML prediction (in practice would use trained model)
        let (strategy, confidence) = if features.num_tensors <= 10 {
            (MLPredictedStrategy::DynamicProgramming, 0.9)
        } else if features.connectivity_density < 0.3 {
            (MLPredictedStrategy::TreeDecomposition, 0.8)
        } else if features.max_bond_dimension > 64 {
            (MLPredictedStrategy::SimulatedAnnealing, 0.7)
        } else {
            (MLPredictedStrategy::Greedy, 0.6)
        };

        let expected_performance = match strategy {
            MLPredictedStrategy::DynamicProgramming => 0.95,
            MLPredictedStrategy::TreeDecomposition => 0.85,
            MLPredictedStrategy::SimulatedAnnealing => 0.75,
            MLPredictedStrategy::Greedy => 0.6,
        };

        Ok(MLPrediction {
            strategy,
            confidence,
            expected_performance,
        })
    }

    fn update_ml_model(
        &self,
        _features: &NetworkFeatures,
        _path: &EnhancedContractionPath,
    ) -> Result<()> {
        // Placeholder for ML model update
        // In practice would update trained model with new data
        Ok(())
    }
}

// Supporting structures for advanced algorithms

#[derive(Debug, Clone)]
struct OptimalIndexOrder {
    tensor1_order: Vec<usize>,
    tensor2_order: Vec<usize>,
}

#[derive(Debug, Clone)]
struct ContractionPlan {
    operations: Vec<ContractionOperation>,
}

#[derive(Debug, Clone)]
struct ContractionOperation {
    tensor1_indices: Vec<usize>,
    tensor2_indices: Vec<usize>,
    result_indices: Vec<usize>,
    operation_type: ContractionOpType,
}

#[derive(Debug, Clone)]
enum ContractionOpType {
    EinsumContraction,
    OuterProduct,
    TraceOperation,
}

#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
struct SciRS2Tensor {
    data: ArrayD<Complex64>,
    shape: Vec<usize>,
}

#[cfg(feature = "advanced_math")]
#[derive(Debug, Clone)]
struct ContractionIndices {
    tensor1_indices: Vec<String>,
    tensor2_indices: Vec<String>,
    common_indices: Vec<String>,
}

#[cfg(feature = "advanced_math")]
impl SciRS2Backend {
    fn einsum_contract(
        &self,
        _tensor1: &SciRS2Tensor,
        _tensor2: &SciRS2Tensor,
        _indices: &ContractionIndices,
    ) -> Result<SciRS2Tensor> {
        // Placeholder for SciRS2 Einstein summation
        Ok(SciRS2Tensor {
            data: ArrayD::zeros(IxDyn(&[2, 2])),
            shape: vec![2, 2],
        })
    }
}

/// Utilities for enhanced tensor networks
pub struct EnhancedTensorNetworkUtils;

impl EnhancedTensorNetworkUtils {
    /// Estimate memory requirements for a tensor network
    pub fn estimate_memory_requirements(
        num_qubits: usize,
        circuit_depth: usize,
        max_bond_dimension: usize,
    ) -> usize {
        // Rough estimate based on typical tensor network structure
        let avg_tensors = num_qubits + circuit_depth;
        let avg_tensor_size = max_bond_dimension.pow(3);
        let memory_per_element = std::mem::size_of::<Complex64>();

        avg_tensors * avg_tensor_size * memory_per_element
    }

    /// Benchmark different contraction strategies
    pub fn benchmark_contraction_strategies(
        num_qubits: usize,
        strategies: &[ContractionStrategy],
    ) -> Result<HashMap<String, f64>> {
        let mut results = HashMap::new();

        for &strategy in strategies {
            let config = EnhancedTensorNetworkConfig {
                contraction_strategy: strategy,
                max_bond_dimension: 64,
                ..Default::default()
            };

            let start_time = std::time::Instant::now();

            // Create and simulate tensor network
            let mut simulator = EnhancedTensorNetworkSimulator::new(config)?;
            simulator.initialize_state(num_qubits)?;

            // Apply some gates for benchmarking
            for i in 0..num_qubits.min(5) {
                let identity = Array2::eye(2);
                simulator.apply_single_qubit_gate(i, &identity)?;
            }

            let execution_time = start_time.elapsed().as_secs_f64();
            results.insert(format!("{:?}", strategy), execution_time);
        }

        Ok(results)
    }

    /// Analyze contraction complexity for a given circuit
    pub fn analyze_contraction_complexity(
        num_qubits: usize,
        gate_structure: &[Vec<usize>], // Gates as lists of qubits they act on
    ) -> (f64, usize) {
        // Estimate FLOP count and memory requirements
        let mut total_flops = 0.0;
        let mut peak_memory = 0;

        for gate_qubits in gate_structure {
            let gate_size = 1 << gate_qubits.len();
            total_flops += (gate_size as f64).powi(3);
            peak_memory = peak_memory.max(gate_size * std::mem::size_of::<Complex64>());
        }

        (total_flops, peak_memory)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use approx::assert_abs_diff_eq;

    #[test]
    fn test_enhanced_tensor_network_config() {
        let config = EnhancedTensorNetworkConfig::default();
        assert_eq!(config.max_bond_dimension, 1024);
        assert_eq!(config.contraction_strategy, ContractionStrategy::Adaptive);
        assert!(config.enable_approximations);
    }

    #[test]
    fn test_tensor_index_creation() {
        let index = TensorIndex {
            label: "q0".to_string(),
            dimension: 2,
            index_type: IndexType::Physical,
            connected_tensors: vec![],
        };

        assert_eq!(index.label, "q0");
        assert_eq!(index.dimension, 2);
        assert_eq!(index.index_type, IndexType::Physical);
    }

    #[test]
    fn test_tensor_network_creation() {
        let mut network = TensorNetwork::new();
        assert_eq!(network.tensor_ids().len(), 0);
        assert_eq!(network.total_size(), 0);
    }

    #[test]
    fn test_enhanced_tensor_creation() {
        let data = Array::zeros(IxDyn(&[2, 2]));
        let indices = vec![
            TensorIndex {
                label: "i0".to_string(),
                dimension: 2,
                index_type: IndexType::Physical,
                connected_tensors: vec![],
            },
            TensorIndex {
                label: "i1".to_string(),
                dimension: 2,
                index_type: IndexType::Physical,
                connected_tensors: vec![],
            },
        ];

        let tensor = EnhancedTensor {
            data,
            indices,
            bond_dimensions: vec![2, 2],
            id: 0,
            memory_size: 4 * std::mem::size_of::<Complex64>(),
            contraction_cost: 8.0,
            priority: 1.0,
        };

        assert_eq!(tensor.bond_dimensions, vec![2, 2]);
        assert_abs_diff_eq!(tensor.contraction_cost, 8.0, epsilon = 1e-10);
    }

    #[test]
    fn test_enhanced_tensor_network_simulator() {
        let config = EnhancedTensorNetworkConfig::default();
        let mut simulator = EnhancedTensorNetworkSimulator::new(config).unwrap();

        simulator.initialize_state(3).unwrap();
        assert_eq!(simulator.network.tensors.len(), 3);
    }

    #[test]
    fn test_contraction_step() {
        let step = ContractionStep {
            tensor_ids: (1, 2),
            result_id: 3,
            flops: 1000.0,
            memory_required: 2048,
            result_dimensions: vec![2, 2],
            parallelizable: true,
        };

        assert_eq!(step.tensor_ids, (1, 2));
        assert_eq!(step.result_id, 3);
        assert_abs_diff_eq!(step.flops, 1000.0, epsilon = 1e-10);
        assert!(step.parallelizable);
    }

    #[test]
    fn test_memory_estimation() {
        let memory = EnhancedTensorNetworkUtils::estimate_memory_requirements(10, 20, 64);
        assert!(memory > 0);
    }

    #[test]
    fn test_contraction_complexity_analysis() {
        let gate_structure = vec![
            vec![0],    // Single-qubit gate on qubit 0
            vec![1],    // Single-qubit gate on qubit 1
            vec![0, 1], // Two-qubit gate on qubits 0,1
        ];

        let (flops, memory) =
            EnhancedTensorNetworkUtils::analyze_contraction_complexity(2, &gate_structure);
        assert!(flops > 0.0);
        assert!(memory > 0);
    }

    #[test]
    fn test_contraction_strategies() {
        let strategies = vec![ContractionStrategy::Greedy, ContractionStrategy::Adaptive];

        // This would fail without proper circuit setup, but tests the interface
        let result = EnhancedTensorNetworkUtils::benchmark_contraction_strategies(3, &strategies);
        // Just verify the function doesn't panic
        assert!(result.is_ok() || result.is_err());
    }

    #[test]
    fn test_enhanced_tensor_network_algorithms() {
        // Test dynamic programming optimization
        let config = EnhancedTensorNetworkConfig::default();
        let simulator = EnhancedTensorNetworkSimulator::new(config).unwrap();

        let tensor_ids = vec![0, 1, 2];
        let dp_result = simulator.optimize_path_dp(&tensor_ids);
        assert!(dp_result.is_ok());

        // Test tree decomposition
        let tree_result = simulator.optimize_path_tree(&tensor_ids);
        assert!(tree_result.is_ok());

        // Test machine learning guided optimization
        let ml_result = simulator.optimize_path_ml(&tensor_ids);
        assert!(ml_result.is_ok());

        // Test network features extraction
        let features_result = simulator.extract_network_features(&tensor_ids);
        assert!(features_result.is_ok());

        let features = features_result.unwrap();
        assert_eq!(features.num_tensors, 3);
        assert!(features.connectivity_density >= 0.0);

        // Test ML strategy prediction
        let prediction_result = simulator.ml_predict_strategy(&features);
        assert!(prediction_result.is_ok());

        let prediction = prediction_result.unwrap();
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
    }
}