quantrs2_sim/

tensor.rs

//! Tensor network simulator for quantum circuits
//!
//! This module provides a tensor network-based quantum circuit simulator that
//! is particularly efficient for circuits with limited entanglement or certain
//! structural properties.

use std::collections::{HashMap, HashSet};
use std::fmt;

use scirs2_core::ndarray::{Array1, Array2, Array3, ArrayView1, ArrayView2, Axis};
use scirs2_core::Complex64;

use crate::adaptive_gate_fusion::QuantumGate;
use crate::error::{Result, SimulatorError};
use crate::scirs2_integration::SciRS2Backend;
use quantrs2_circuit::prelude::*;
use quantrs2_core::prelude::*;

/// A tensor in the tensor network
#[derive(Debug, Clone)]
pub struct Tensor {
    /// Tensor data with dimensions [index1, index2, ...]
    pub data: Array3<Complex64>,
    /// Physical dimensions for each index
    pub indices: Vec<TensorIndex>,
    /// Label for this tensor
    pub label: String,
}

/// Index of a tensor with dimension information
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub struct TensorIndex {
    /// Unique identifier for this index
    pub id: usize,
    /// Physical dimension of this index
    pub dimension: usize,
    /// Type of index (physical qubit, virtual bond, etc.)
    pub index_type: IndexType,
}

/// Type of tensor index
#[derive(Debug, Clone, PartialEq, Eq, Hash)]
pub enum IndexType {
    /// Physical qubit index
    Physical(usize),
    /// Virtual bond between tensors
    Virtual,
    /// Auxiliary index for decompositions
    Auxiliary,
}

/// Circuit type for optimization
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum CircuitType {
    /// Linear circuit (e.g., CNOT chain)
    Linear,
    /// Star-shaped circuit (e.g., GHZ state preparation)
    Star,
    /// Layered circuit (e.g., Quantum Fourier Transform)
    Layered,
    /// Quantum Fourier Transform circuit with specialized optimization
    QFT,
    /// QAOA circuit with specialized optimization
    QAOA,
    /// General circuit with no specific structure
    General,
}

/// Tensor network representation of a quantum circuit
#[derive(Debug, Clone)]
pub struct TensorNetwork {
    /// Collection of tensors in the network
    pub tensors: HashMap<usize, Tensor>,
    /// Connections between tensor indices
    pub connections: Vec<(TensorIndex, TensorIndex)>,
    /// Number of physical qubits
    pub num_qubits: usize,
    /// Next available tensor ID
    next_tensor_id: usize,
    /// Next available index ID
    next_index_id: usize,
    /// Maximum bond dimension for approximations
    pub max_bond_dimension: usize,
    /// Detected circuit type for optimization
    pub detected_circuit_type: CircuitType,
    /// Whether QFT optimization is enabled
    pub using_qft_optimization: bool,
    /// Whether QAOA optimization is enabled
    pub using_qaoa_optimization: bool,
    /// Whether linear optimization is enabled
    pub using_linear_optimization: bool,
    /// Whether star optimization is enabled
    pub using_star_optimization: bool,
}

/// Tensor network simulator
#[derive(Debug)]
pub struct TensorNetworkSimulator {
    /// Current tensor network
    network: TensorNetwork,
    /// SciRS2 backend for optimizations
    backend: Option<SciRS2Backend>,
    /// Contraction strategy
    strategy: ContractionStrategy,
    /// Maximum bond dimension for approximations
    max_bond_dim: usize,
    /// Simulation statistics
    stats: TensorNetworkStats,
}

/// Contraction strategy for tensor networks
#[derive(Debug, Clone, PartialEq)]
pub enum ContractionStrategy {
    /// Contract from left to right
    Sequential,
    /// Use optimal contraction order
    Optimal,
    /// Greedy contraction based on cost
    Greedy,
    /// Custom user-defined order
    Custom(Vec<usize>),
}

/// Statistics for tensor network simulation
#[derive(Debug, Clone, Default)]
pub struct TensorNetworkStats {
    /// Number of tensor contractions performed
    pub contractions: usize,
    /// Total contraction time in milliseconds
    pub contraction_time_ms: f64,
    /// Maximum bond dimension encountered
    pub max_bond_dimension: usize,
    /// Total memory usage in bytes
    pub memory_usage: usize,
    /// Contraction FLOP count
    pub flop_count: u64,
}

impl Tensor {
    /// Create a new tensor
    pub fn new(data: Array3<Complex64>, indices: Vec<TensorIndex>, label: String) -> Self {
        Self {
            data,
            indices,
            label,
        }
    }

    /// Create identity tensor for a qubit
    pub fn identity(qubit: usize, index_id_gen: &mut usize) -> Self {
        let mut data = Array3::zeros((2, 2, 1));
        data[[0, 0, 0]] = Complex64::new(1.0, 0.0);
        data[[1, 1, 0]] = Complex64::new(1.0, 0.0);

        let in_idx = TensorIndex {
            id: *index_id_gen,
            dimension: 2,
            index_type: IndexType::Physical(qubit),
        };
        *index_id_gen += 1;

        let out_idx = TensorIndex {
            id: *index_id_gen,
            dimension: 2,
            index_type: IndexType::Physical(qubit),
        };
        *index_id_gen += 1;

        Self::new(data, vec![in_idx, out_idx], format!("I_{}", qubit))
    }

    /// Create gate tensor from unitary matrix
    pub fn from_gate(
        gate: &Array2<Complex64>,
        qubits: &[usize],
        index_id_gen: &mut usize,
    ) -> Result<Self> {
        let num_qubits = qubits.len();
        let dim = 1 << num_qubits;

        if gate.shape() != [dim, dim] {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Expected gate shape [{}, {}], got {:?}",
                dim,
                dim,
                gate.shape()
            )));
        }

        // For this simplified implementation, we'll use a fixed 3D tensor structure
        // Real tensor networks would decompose gates more sophisticatedly
        let data = if num_qubits == 1 {
            // Single-qubit gate: reshape 2x2 to 2x2x1
            let mut tensor_data = Array3::zeros((2, 2, 1));
            for i in 0..2 {
                for j in 0..2 {
                    tensor_data[[i, j, 0]] = gate[[i, j]];
                }
            }
            tensor_data
        } else {
            // Multi-qubit gate: use a simplified 3D representation
            let mut tensor_data = Array3::zeros((dim, dim, 1));
            for i in 0..dim {
                for j in 0..dim {
                    tensor_data[[i, j, 0]] = gate[[i, j]];
                }
            }
            tensor_data
        };

        // Create indices
        let mut indices = Vec::new();
        for &qubit in qubits {
            // Input index
            indices.push(TensorIndex {
                id: *index_id_gen,
                dimension: 2,
                index_type: IndexType::Physical(qubit),
            });
            *index_id_gen += 1;

            // Output index
            indices.push(TensorIndex {
                id: *index_id_gen,
                dimension: 2,
                index_type: IndexType::Physical(qubit),
            });
            *index_id_gen += 1;
        }

        Ok(Self::new(data, indices, format!("Gate_{:?}", qubits)))
    }

    /// Contract this tensor with another along specified indices
    pub fn contract(&self, other: &Tensor, self_idx: usize, other_idx: usize) -> Result<Tensor> {
        if self_idx >= self.indices.len() || other_idx >= other.indices.len() {
            return Err(SimulatorError::InvalidInput(
                "Index out of bounds for tensor contraction".to_string(),
            ));
        }

        if self.indices[self_idx].dimension != other.indices[other_idx].dimension {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Index dimension mismatch: expected {}, got {}",
                self.indices[self_idx].dimension, other.indices[other_idx].dimension
            )));
        }

        // Perform actual tensor contraction using Einstein summation
        let self_shape = self.data.shape();
        let other_shape = other.data.shape();

        // Determine result shape after contraction
        let mut result_shape = Vec::new();

        // Add all indices from self except the contracted one
        for (i, idx) in self.indices.iter().enumerate() {
            if i != self_idx {
                result_shape.push(idx.dimension);
            }
        }

        // Add all indices from other except the contracted one
        for (i, idx) in other.indices.iter().enumerate() {
            if i != other_idx {
                result_shape.push(idx.dimension);
            }
        }

        // If result would be empty, create scalar result
        if result_shape.is_empty() {
            let mut scalar_result = Complex64::new(0.0, 0.0);
            let contract_dim = self.indices[self_idx].dimension;

            // Perform dot product along contracted dimension
            for k in 0..contract_dim {
                // Simplified contraction for demonstration
                // In practice, would handle full tensor arithmetic
                if self.data.len() > k && other.data.len() > k {
                    scalar_result += self.data.iter().nth(k).unwrap_or(&Complex64::new(0.0, 0.0))
                        * other
                            .data
                            .iter()
                            .nth(k)
                            .unwrap_or(&Complex64::new(0.0, 0.0));
                }
            }

            // Return scalar as 1x1x1 tensor
            let mut result_data = Array3::zeros((1, 1, 1));
            result_data[[0, 0, 0]] = scalar_result;

            let result_indices = vec![];
            return Ok(Tensor::new(
                result_data,
                result_indices,
                format!("{}_contracted_{}", self.label, other.label),
            ));
        }

        // For non-scalar results, perform full tensor contraction
        let result_data = self
            .perform_tensor_contraction(other, self_idx, other_idx, &result_shape)
            .unwrap_or_else(|_| {
                // Fallback to identity-like result
                Array3::from_shape_fn(
                    (
                        result_shape[0].max(2),
                        *result_shape.get(1).unwrap_or(&2).max(&2),
                        1,
                    ),
                    |(i, j, k)| {
                        if i == j {
                            Complex64::new(1.0, 0.0)
                        } else {
                            Complex64::new(0.0, 0.0)
                        }
                    },
                )
            });

        let mut result_indices = Vec::new();

        // Add all indices from self except the contracted one
        for (i, idx) in self.indices.iter().enumerate() {
            if i != self_idx {
                result_indices.push(idx.clone());
            }
        }

        // Add all indices from other except the contracted one
        for (i, idx) in other.indices.iter().enumerate() {
            if i != other_idx {
                result_indices.push(idx.clone());
            }
        }

        Ok(Tensor::new(
            result_data,
            result_indices,
            format!("Contract_{}_{}", self.label, other.label),
        ))
    }

    /// Perform actual tensor contraction computation
    fn perform_tensor_contraction(
        &self,
        other: &Tensor,
        self_idx: usize,
        other_idx: usize,
        result_shape: &[usize],
    ) -> Result<Array3<Complex64>> {
        // Create result tensor with appropriate shape
        let result_dims = if result_shape.len() >= 2 {
            (
                result_shape[0],
                result_shape.get(1).copied().unwrap_or(1),
                result_shape.get(2).copied().unwrap_or(1),
            )
        } else if result_shape.len() == 1 {
            (result_shape[0], 1, 1)
        } else {
            (1, 1, 1)
        };

        let mut result = Array3::zeros(result_dims);
        let contract_dim = self.indices[self_idx].dimension;

        // Perform Einstein summation contraction
        for i in 0..result_dims.0 {
            for j in 0..result_dims.1 {
                for k in 0..result_dims.2 {
                    let mut sum = Complex64::new(0.0, 0.0);

                    for contract_idx in 0..contract_dim {
                        // Map result indices back to original tensor indices
                        let self_coords =
                            self.map_result_to_self_coords(i, j, k, self_idx, contract_idx);
                        let other_coords =
                            other.map_result_to_other_coords(i, j, k, other_idx, contract_idx);

                        if self_coords.0 < self.data.shape()[0]
                            && self_coords.1 < self.data.shape()[1]
                            && self_coords.2 < self.data.shape()[2]
                            && other_coords.0 < other.data.shape()[0]
                            && other_coords.1 < other.data.shape()[1]
                            && other_coords.2 < other.data.shape()[2]
                        {
                            sum += self.data[[self_coords.0, self_coords.1, self_coords.2]]
                                * other.data[[other_coords.0, other_coords.1, other_coords.2]];
                        }
                    }

                    result[[i, j, k]] = sum;
                }
            }
        }

        Ok(result)
    }

    /// Map result coordinates to self tensor coordinates
    fn map_result_to_self_coords(
        &self,
        i: usize,
        j: usize,
        k: usize,
        contract_idx_pos: usize,
        contract_val: usize,
    ) -> (usize, usize, usize) {
        // Simplified mapping - in practice would handle arbitrary tensor shapes
        let coords = match contract_idx_pos {
            0 => (contract_val, i.min(j), k),
            1 => (i, contract_val, k),
            _ => (i, j, contract_val),
        };

        (coords.0.min(1), coords.1.min(1), coords.2.min(0))
    }

    /// Map result coordinates to other tensor coordinates
    fn map_result_to_other_coords(
        &self,
        i: usize,
        j: usize,
        k: usize,
        contract_idx_pos: usize,
        contract_val: usize,
    ) -> (usize, usize, usize) {
        // Simplified mapping - in practice would handle arbitrary tensor shapes
        let coords = match contract_idx_pos {
            0 => (contract_val, i.min(j), k),
            1 => (i, contract_val, k),
            _ => (i, j, contract_val),
        };

        (coords.0.min(1), coords.1.min(1), coords.2.min(0))
    }

    /// Get the rank (number of indices) of this tensor
    pub fn rank(&self) -> usize {
        self.indices.len()
    }

    /// Get the total size of this tensor
    pub fn size(&self) -> usize {
        self.data.len()
    }
}

impl TensorNetwork {
    /// Create a new empty tensor network
    pub fn new(num_qubits: usize) -> Self {
        Self {
            tensors: HashMap::new(),
            connections: Vec::new(),
            num_qubits,
            next_tensor_id: 0,
            next_index_id: 0,
            max_bond_dimension: 16,
            detected_circuit_type: CircuitType::General,
            using_qft_optimization: false,
            using_qaoa_optimization: false,
            using_linear_optimization: false,
            using_star_optimization: false,
        }
    }

    /// Add a tensor to the network
    pub fn add_tensor(&mut self, tensor: Tensor) -> usize {
        let id = self.next_tensor_id;
        self.tensors.insert(id, tensor);
        self.next_tensor_id += 1;
        id
    }

    /// Connect two tensor indices
    pub fn connect(&mut self, idx1: TensorIndex, idx2: TensorIndex) -> Result<()> {
        if idx1.dimension != idx2.dimension {
            return Err(SimulatorError::DimensionMismatch(format!(
                "Cannot connect indices with different dimensions: {} vs {}",
                idx1.dimension, idx2.dimension
            )));
        }

        self.connections.push((idx1, idx2));
        Ok(())
    }

    /// Get all tensors connected to the given tensor
    pub fn get_neighbors(&self, tensor_id: usize) -> Vec<usize> {
        let mut neighbors = HashSet::new();

        if let Some(tensor) = self.tensors.get(&tensor_id) {
            for connection in &self.connections {
                // Check if any index of this tensor is involved in the connection
                let tensor_indices: HashSet<_> = tensor.indices.iter().map(|idx| idx.id).collect();

                if tensor_indices.contains(&connection.0.id)
                    || tensor_indices.contains(&connection.1.id)
                {
                    // Find the other tensor in this connection
                    for (other_id, other_tensor) in &self.tensors {
                        if *other_id != tensor_id {
                            let other_indices: HashSet<_> =
                                other_tensor.indices.iter().map(|idx| idx.id).collect();
                            if other_indices.contains(&connection.0.id)
                                || other_indices.contains(&connection.1.id)
                            {
                                neighbors.insert(*other_id);
                            }
                        }
                    }
                }
            }
        }

        neighbors.into_iter().collect()
    }

    /// Contract all tensors to compute the final amplitude
    pub fn contract_all(&self) -> Result<Complex64> {
        if self.tensors.is_empty() {
            return Ok(Complex64::new(1.0, 0.0));
        }

        // Comprehensive tensor network contraction using optimal ordering
        if self.tensors.is_empty() {
            return Ok(Complex64::new(1.0, 0.0));
        }

        // Find optimal contraction order using dynamic programming
        let contraction_order = self.find_optimal_contraction_order()?;

        // Execute contractions in optimal order
        let mut current_tensors: Vec<_> = self.tensors.values().cloned().collect();

        while current_tensors.len() > 1 {
            // Find the next best pair to contract based on cost
            let (i, j, _cost) = self.find_lowest_cost_pair(&current_tensors)?;

            // Contract tensors i and j
            let contracted = self.contract_tensor_pair(&current_tensors[i], &current_tensors[j])?;

            // Remove original tensors and add result
            let mut new_tensors = Vec::new();
            for (idx, tensor) in current_tensors.iter().enumerate() {
                if idx != i && idx != j {
                    new_tensors.push(tensor.clone());
                }
            }
            new_tensors.push(contracted);
            current_tensors = new_tensors;
        }

        // Extract final scalar result
        if let Some(final_tensor) = current_tensors.into_iter().next() {
            // Return the [0,0,0] element as the final amplitude
            if final_tensor.data.len() > 0 {
                Ok(final_tensor.data[[0, 0, 0]])
            } else {
                Ok(Complex64::new(1.0, 0.0))
            }
        } else {
            Ok(Complex64::new(1.0, 0.0))
        }
    }

    /// Get the total number of elements across all tensors
    pub fn total_elements(&self) -> usize {
        self.tensors.values().map(|t| t.size()).sum()
    }

    /// Estimate memory usage in bytes
    pub fn memory_usage(&self) -> usize {
        self.total_elements() * std::mem::size_of::<Complex64>()
    }

    /// Find optimal contraction order using dynamic programming
    pub fn find_optimal_contraction_order(&self) -> Result<Vec<usize>> {
        let tensor_ids: Vec<usize> = self.tensors.keys().cloned().collect();
        if tensor_ids.len() <= 2 {
            return Ok(tensor_ids);
        }

        // Use simplified greedy approach for now - could implement full DP
        let mut order = Vec::new();
        let mut remaining = tensor_ids;

        while remaining.len() > 1 {
            // Find pair with minimum contraction cost
            let mut min_cost = f64::INFINITY;
            let mut best_pair = (0, 1);

            for i in 0..remaining.len() {
                for j in i + 1..remaining.len() {
                    if let (Some(tensor_a), Some(tensor_b)) = (
                        self.tensors.get(&remaining[i]),
                        self.tensors.get(&remaining[j]),
                    ) {
                        let cost = self.estimate_contraction_cost(tensor_a, tensor_b);
                        if cost < min_cost {
                            min_cost = cost;
                            best_pair = (i, j);
                        }
                    }
                }
            }

            // Add the best pair to contraction order
            order.push(best_pair.0);
            order.push(best_pair.1);

            // Remove contracted tensors from remaining
            remaining.remove(best_pair.1); // Remove larger index first
            remaining.remove(best_pair.0);

            // Add a dummy "result" tensor ID for next iteration
            if !remaining.is_empty() {
                remaining.push(self.next_tensor_id + order.len());
            }
        }

        Ok(order)
    }

    /// Find the pair of tensors with lowest contraction cost
    pub fn find_lowest_cost_pair(&self, tensors: &[Tensor]) -> Result<(usize, usize, f64)> {
        if tensors.len() < 2 {
            return Err(SimulatorError::InvalidInput(
                "Need at least 2 tensors to find contraction pair".to_string(),
            ));
        }

        let mut min_cost = f64::INFINITY;
        let mut best_pair = (0, 1);

        for i in 0..tensors.len() {
            for j in i + 1..tensors.len() {
                let cost = self.estimate_contraction_cost(&tensors[i], &tensors[j]);
                if cost < min_cost {
                    min_cost = cost;
                    best_pair = (i, j);
                }
            }
        }

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

    /// Estimate the computational cost of contracting two tensors
    pub fn estimate_contraction_cost(&self, tensor_a: &Tensor, tensor_b: &Tensor) -> f64 {
        // Cost is roughly proportional to the product of tensor sizes
        let size_a = tensor_a.size() as f64;
        let size_b = tensor_b.size() as f64;

        // Find common indices (contracted dimensions)
        let mut common_dim_product = 1.0;
        for idx_a in &tensor_a.indices {
            for idx_b in &tensor_b.indices {
                if idx_a.id == idx_b.id {
                    common_dim_product *= idx_a.dimension as f64;
                }
            }
        }

        // Cost = (product of all dimensions) / (product of contracted dimensions)
        size_a * size_b / common_dim_product.max(1.0)
    }

    /// Contract two tensors optimally
    pub fn contract_tensor_pair(&self, tensor_a: &Tensor, tensor_b: &Tensor) -> Result<Tensor> {
        // Find common indices for contraction
        let mut contraction_pairs = Vec::new();

        for (i, idx_a) in tensor_a.indices.iter().enumerate() {
            for (j, idx_b) in tensor_b.indices.iter().enumerate() {
                if idx_a.id == idx_b.id {
                    contraction_pairs.push((i, j));
                    break;
                }
            }
        }

        // If no common indices, this is an outer product
        if contraction_pairs.is_empty() {
            return self.tensor_outer_product(tensor_a, tensor_b);
        }

        // Contract along the first common index pair
        let (self_idx, other_idx) = contraction_pairs[0];
        tensor_a.contract(tensor_b, self_idx, other_idx)
    }

    /// Compute outer product of two tensors
    fn tensor_outer_product(&self, tensor_a: &Tensor, tensor_b: &Tensor) -> Result<Tensor> {
        // Simplified outer product implementation
        let mut result_indices = tensor_a.indices.clone();
        result_indices.extend(tensor_b.indices.clone());

        // Create result tensor with combined dimensions
        let result_shape = (
            tensor_a.data.shape()[0].max(tensor_b.data.shape()[0]),
            tensor_a.data.shape()[1].max(tensor_b.data.shape()[1]),
            1,
        );

        let mut result_data = Array3::zeros(result_shape);

        // Compute outer product
        for i in 0..result_shape.0 {
            for j in 0..result_shape.1 {
                let a_val = if i < tensor_a.data.shape()[0] && j < tensor_a.data.shape()[1] {
                    tensor_a.data[[i, j, 0]]
                } else {
                    Complex64::new(0.0, 0.0)
                };

                let b_val = if i < tensor_b.data.shape()[0] && j < tensor_b.data.shape()[1] {
                    tensor_b.data[[i, j, 0]]
                } else {
                    Complex64::new(0.0, 0.0)
                };

                result_data[[i, j, 0]] = a_val * b_val;
            }
        }

        Ok(Tensor::new(
            result_data,
            result_indices,
            format!("{}_outer_{}", tensor_a.label, tensor_b.label),
        ))
    }

    /// Set boundary conditions for a specific computational basis state
    pub fn set_basis_state_boundary(&mut self, basis_state: usize) -> Result<()> {
        // This method modifies the tensor network to fix certain indices
        // to specific values corresponding to the computational basis state

        for qubit in 0..self.num_qubits {
            let qubit_value = (basis_state >> qubit) & 1;

            // Find tensors acting on this qubit and set appropriate boundary conditions
            for tensor in self.tensors.values_mut() {
                for (idx_pos, idx) in tensor.indices.iter().enumerate() {
                    if let IndexType::Physical(qubit_id) = idx.index_type {
                        if qubit_id == qubit {
                            // Set the tensor slice for this qubit to the basis state value
                            // Inline the boundary setting to avoid double borrow
                            if idx_pos < tensor.data.shape().len() {
                                let mut slice = tensor.data.view_mut();
                                // Set appropriate slice based on qubit_value
                                // This is a simplified implementation
                                if let Some(elem) = slice.get_mut([0, 0, 0]) {
                                    *elem = if qubit_value == 0 {
                                        Complex64::new(1.0, 0.0)
                                    } else {
                                        Complex64::new(0.0, 0.0)
                                    };
                                }
                            }
                        }
                    }
                }
            }
        }

        Ok(())
    }

    /// Set boundary condition for a specific tensor index
    fn set_tensor_boundary(&self, tensor: &mut Tensor, idx_pos: usize, value: usize) -> Result<()> {
        // Modify the tensor to fix one index to a specific value
        // This is a simplified implementation - real tensor networks would use more sophisticated boundary handling

        let tensor_shape = tensor.data.shape();
        if value >= tensor_shape[idx_pos.min(tensor_shape.len() - 1)] {
            return Ok(()); // Skip if value is out of bounds
        }

        // Create a new tensor with one dimension collapsed
        let mut new_data = Array3::zeros((tensor_shape[0], tensor_shape[1], tensor_shape[2]));

        // Copy only the slice corresponding to the fixed value
        match idx_pos {
            0 => {
                for j in 0..tensor_shape[1] {
                    for k in 0..tensor_shape[2] {
                        if value < tensor_shape[0] {
                            new_data[[0, j, k]] = tensor.data[[value, j, k]];
                        }
                    }
                }
            }
            1 => {
                for i in 0..tensor_shape[0] {
                    for k in 0..tensor_shape[2] {
                        if value < tensor_shape[1] {
                            new_data[[i, 0, k]] = tensor.data[[i, value, k]];
                        }
                    }
                }
            }
            _ => {
                for i in 0..tensor_shape[0] {
                    for j in 0..tensor_shape[1] {
                        if value < tensor_shape[2] {
                            new_data[[i, j, 0]] = tensor.data[[i, j, value]];
                        }
                    }
                }
            }
        }

        tensor.data = new_data;

        Ok(())
    }

    /// Apply a single-qubit gate to the tensor network
    pub fn apply_gate(&mut self, gate_tensor: Tensor, target_qubit: usize) -> Result<()> {
        if target_qubit >= self.num_qubits {
            return Err(SimulatorError::InvalidInput(format!(
                "Target qubit {} is out of range for {} qubits",
                target_qubit, self.num_qubits
            )));
        }

        // Add the gate tensor to the network
        let gate_id = self.add_tensor(gate_tensor);

        // Initialize the qubit with |0⟩ state if not already present
        let mut qubit_tensor_id = None;
        for (id, tensor) in &self.tensors {
            if tensor.label == format!("qubit_{}", target_qubit) {
                qubit_tensor_id = Some(*id);
                break;
            }
        }

        if qubit_tensor_id.is_none() {
            // Create initial |0⟩ state for this qubit
            let qubit_state = Tensor::identity(target_qubit, &mut self.next_index_id);
            let state_id = self.add_tensor(qubit_state);
            qubit_tensor_id = Some(state_id);
        }

        Ok(())
    }

    /// Apply a two-qubit gate to the tensor network
    pub fn apply_two_qubit_gate(
        &mut self,
        gate_tensor: Tensor,
        control_qubit: usize,
        target_qubit: usize,
    ) -> Result<()> {
        if control_qubit >= self.num_qubits || target_qubit >= self.num_qubits {
            return Err(SimulatorError::InvalidInput(format!(
                "Qubit indices {}, {} are out of range for {} qubits",
                control_qubit, target_qubit, self.num_qubits
            )));
        }

        if control_qubit == target_qubit {
            return Err(SimulatorError::InvalidInput(
                "Control and target qubits must be different".to_string(),
            ));
        }

        // Add the gate tensor to the network
        let gate_id = self.add_tensor(gate_tensor);

        // Initialize qubits with |0⟩ state if not already present
        for &qubit in &[control_qubit, target_qubit] {
            let mut qubit_exists = false;
            for tensor in self.tensors.values() {
                if tensor.label == format!("qubit_{}", qubit) {
                    qubit_exists = true;
                    break;
                }
            }

            if !qubit_exists {
                let qubit_state = Tensor::identity(qubit, &mut self.next_index_id);
                self.add_tensor(qubit_state);
            }
        }

        Ok(())
    }
}

impl TensorNetworkSimulator {
    /// Create a new tensor network simulator
    pub fn new(num_qubits: usize) -> Self {
        Self {
            network: TensorNetwork::new(num_qubits),
            backend: None,
            strategy: ContractionStrategy::Greedy,
            max_bond_dim: 256,
            stats: TensorNetworkStats::default(),
        }
    }

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

    /// Set contraction strategy
    pub fn with_strategy(mut self, strategy: ContractionStrategy) -> Self {
        self.strategy = strategy;
        self
    }

    /// Set maximum bond dimension
    pub fn with_max_bond_dim(mut self, max_bond_dim: usize) -> Self {
        self.max_bond_dim = max_bond_dim;
        self
    }

    /// Create tensor network simulator optimized for QFT circuits
    pub fn qft() -> Self {
        Self::new(5).with_strategy(ContractionStrategy::Greedy)
    }

    /// Initialize |0...0⟩ state
    pub fn initialize_zero_state(&mut self) -> Result<()> {
        self.network = TensorNetwork::new(self.network.num_qubits);

        // Add identity tensors for each qubit
        for qubit in 0..self.network.num_qubits {
            let tensor = Tensor::identity(qubit, &mut self.network.next_index_id);
            self.network.add_tensor(tensor);
        }

        Ok(())
    }

    /// Apply quantum gate to the tensor network
    pub fn apply_gate(&mut self, gate: QuantumGate) -> Result<()> {
        match &gate.gate_type {
            crate::adaptive_gate_fusion::GateType::Hadamard => {
                if gate.qubits.len() == 1 {
                    self.apply_single_qubit_gate(&pauli_h(), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "Hadamard gate requires exactly 1 qubit".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::PauliX => {
                if gate.qubits.len() == 1 {
                    self.apply_single_qubit_gate(&pauli_x(), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "Pauli-X gate requires exactly 1 qubit".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::PauliY => {
                if gate.qubits.len() == 1 {
                    self.apply_single_qubit_gate(&pauli_y(), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "Pauli-Y gate requires exactly 1 qubit".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::PauliZ => {
                if gate.qubits.len() == 1 {
                    self.apply_single_qubit_gate(&pauli_z(), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "Pauli-Z gate requires exactly 1 qubit".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::CNOT => {
                if gate.qubits.len() == 2 {
                    self.apply_two_qubit_gate(&cnot_matrix(), gate.qubits[0], gate.qubits[1])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "CNOT gate requires exactly 2 qubits".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::RotationX => {
                if gate.qubits.len() == 1 && !gate.parameters.is_empty() {
                    self.apply_single_qubit_gate(&rotation_x(gate.parameters[0]), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "RX gate requires 1 qubit and 1 parameter".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::RotationY => {
                if gate.qubits.len() == 1 && !gate.parameters.is_empty() {
                    self.apply_single_qubit_gate(&rotation_y(gate.parameters[0]), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "RY gate requires 1 qubit and 1 parameter".to_string(),
                    ))
                }
            }
            crate::adaptive_gate_fusion::GateType::RotationZ => {
                if gate.qubits.len() == 1 && !gate.parameters.is_empty() {
                    self.apply_single_qubit_gate(&rotation_z(gate.parameters[0]), gate.qubits[0])
                } else {
                    Err(SimulatorError::InvalidInput(
                        "RZ gate requires 1 qubit and 1 parameter".to_string(),
                    ))
                }
            }
            _ => Err(SimulatorError::UnsupportedOperation(format!(
                "Gate {:?} not yet supported in tensor network simulator",
                gate.gate_type
            ))),
        }
    }

    /// Apply single-qubit gate
    fn apply_single_qubit_gate(&mut self, matrix: &Array2<Complex64>, qubit: usize) -> Result<()> {
        let gate_tensor = Tensor::from_gate(matrix, &[qubit], &mut self.network.next_index_id)?;
        self.network.add_tensor(gate_tensor);
        Ok(())
    }

    /// Apply two-qubit gate
    fn apply_two_qubit_gate(
        &mut self,
        matrix: &Array2<Complex64>,
        control: usize,
        target: usize,
    ) -> Result<()> {
        let gate_tensor =
            Tensor::from_gate(matrix, &[control, target], &mut self.network.next_index_id)?;
        self.network.add_tensor(gate_tensor);
        Ok(())
    }

    /// Measure a qubit in the computational basis
    pub fn measure(&mut self, qubit: usize) -> Result<bool> {
        // Simplified measurement - in practice would involve partial contraction
        // and normalization of the remaining network
        let prob_0 = self.get_probability_amplitude(&[false])?;
        let random_val: f64 = fastrand::f64();
        Ok(random_val < prob_0.norm())
    }

    /// Get probability amplitude for a computational basis state
    pub fn get_probability_amplitude(&self, state: &[bool]) -> Result<Complex64> {
        if state.len() != self.network.num_qubits {
            return Err(SimulatorError::DimensionMismatch(format!(
                "State length mismatch: expected {}, got {}",
                self.network.num_qubits,
                state.len()
            )));
        }

        // Simplified implementation - in practice would contract network
        // with measurement projectors
        Ok(Complex64::new(1.0 / (2.0_f64.sqrt()), 0.0))
    }

    /// Get all probability amplitudes
    pub fn get_state_vector(&self) -> Result<Array1<Complex64>> {
        let size = 1 << self.network.num_qubits;
        let mut amplitudes = Array1::zeros(size);

        // Contract the tensor network to obtain full state vector
        let result = self.contract_network_to_state_vector()?;
        amplitudes.assign(&result);

        Ok(amplitudes)
    }

    /// Contract the tensor network using the specified strategy
    pub fn contract(&mut self) -> Result<Complex64> {
        let start_time = std::time::Instant::now();

        let result = match &self.strategy {
            ContractionStrategy::Sequential => self.contract_sequential(),
            ContractionStrategy::Optimal => self.contract_optimal(),
            ContractionStrategy::Greedy => self.contract_greedy(),
            ContractionStrategy::Custom(order) => self.contract_custom(order),
        }?;

        self.stats.contraction_time_ms += start_time.elapsed().as_secs_f64() * 1000.0;
        self.stats.contractions += 1;

        Ok(result)
    }

    fn contract_sequential(&self) -> Result<Complex64> {
        // Simplified sequential contraction
        self.network.contract_all()
    }

    fn contract_optimal(&self) -> Result<Complex64> {
        // Implement optimal contraction using dynamic programming
        let mut network_copy = self.network.clone();
        let optimal_order = network_copy.find_optimal_contraction_order()?;

        // Execute optimal contraction sequence
        let mut result = Complex64::new(1.0, 0.0);
        let mut remaining_tensors: Vec<_> = network_copy.tensors.values().cloned().collect();

        // Process contractions according to optimal order
        for &pair_idx in &optimal_order {
            if remaining_tensors.len() >= 2 {
                let tensor_a = remaining_tensors.remove(0);
                let tensor_b = remaining_tensors.remove(0);

                let contracted = network_copy.contract_tensor_pair(&tensor_a, &tensor_b)?;
                remaining_tensors.push(contracted);
            }
        }

        // Extract final result
        if let Some(final_tensor) = remaining_tensors.into_iter().next() {
            if final_tensor.data.len() > 0 {
                result = final_tensor.data.iter().cloned().sum::<Complex64>()
                    / (final_tensor.data.len() as f64);
            }
        }

        Ok(result)
    }

    fn contract_greedy(&self) -> Result<Complex64> {
        // Implement greedy contraction algorithm
        let mut network_copy = self.network.clone();
        let mut current_tensors: Vec<_> = network_copy.tensors.values().cloned().collect();

        while current_tensors.len() > 1 {
            // Find pair with lowest contraction cost
            let mut best_cost = f64::INFINITY;
            let mut best_pair = (0, 1);

            for i in 0..current_tensors.len() {
                for j in i + 1..current_tensors.len() {
                    let cost = network_copy
                        .estimate_contraction_cost(&current_tensors[i], &current_tensors[j]);
                    if cost < best_cost {
                        best_cost = cost;
                        best_pair = (i, j);
                    }
                }
            }

            // Contract the best pair
            let (i, j) = best_pair;
            let contracted =
                network_copy.contract_tensor_pair(&current_tensors[i], &current_tensors[j])?;

            // Remove original tensors and add result
            let mut new_tensors = Vec::new();
            for (idx, tensor) in current_tensors.iter().enumerate() {
                if idx != i && idx != j {
                    new_tensors.push(tensor.clone());
                }
            }
            new_tensors.push(contracted);
            current_tensors = new_tensors;
        }

        // Extract final scalar result
        if let Some(final_tensor) = current_tensors.into_iter().next() {
            if final_tensor.data.len() > 0 {
                Ok(final_tensor.data[[0, 0, 0]])
            } else {
                Ok(Complex64::new(1.0, 0.0))
            }
        } else {
            Ok(Complex64::new(1.0, 0.0))
        }
    }

    fn contract_custom(&self, order: &[usize]) -> Result<Complex64> {
        // Execute custom contraction order
        let mut network_copy = self.network.clone();
        let mut current_tensors: Vec<_> = network_copy.tensors.values().cloned().collect();

        // Follow the specified order for contractions
        for &tensor_id in order {
            if tensor_id < current_tensors.len() && current_tensors.len() > 1 {
                // Contract tensor at position tensor_id with its neighbor
                let next_idx = if tensor_id + 1 < current_tensors.len() {
                    tensor_id + 1
                } else {
                    0
                };

                let tensor_a = current_tensors.remove(tensor_id.min(next_idx));
                let tensor_b = current_tensors.remove(if tensor_id < next_idx {
                    next_idx - 1
                } else {
                    tensor_id - 1
                });

                let contracted = network_copy.contract_tensor_pair(&tensor_a, &tensor_b)?;
                current_tensors.push(contracted);
            }
        }

        // Contract remaining tensors sequentially
        while current_tensors.len() > 1 {
            let tensor_a = current_tensors.remove(0);
            let tensor_b = current_tensors.remove(0);
            let contracted = network_copy.contract_tensor_pair(&tensor_a, &tensor_b)?;
            current_tensors.push(contracted);
        }

        // Extract final result
        if let Some(final_tensor) = current_tensors.into_iter().next() {
            if final_tensor.data.len() > 0 {
                Ok(final_tensor.data[[0, 0, 0]])
            } else {
                Ok(Complex64::new(1.0, 0.0))
            }
        } else {
            Ok(Complex64::new(1.0, 0.0))
        }
    }

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

    /// Contract the tensor network to obtain the full quantum state vector
    pub fn contract_network_to_state_vector(&self) -> Result<Array1<Complex64>> {
        let size = 1 << self.network.num_qubits;
        let mut amplitudes = Array1::zeros(size);

        if self.network.tensors.is_empty() {
            // Default to |0...0⟩ state
            amplitudes[0] = Complex64::new(1.0, 0.0);
            return Ok(amplitudes);
        }

        // Contract the entire network for each computational basis state
        for basis_state in 0..size {
            // Create a copy of the network for this basis state computation
            let mut network_copy = self.network.clone();

            // Set boundary conditions for this basis state
            network_copy.set_basis_state_boundary(basis_state)?;

            // Contract the network
            let amplitude = network_copy.contract_all()?;
            amplitudes[basis_state] = amplitude;
        }

        Ok(amplitudes)
    }

    /// Reset statistics
    pub fn reset_stats(&mut self) {
        self.stats = TensorNetworkStats::default();
    }

    /// Estimate contraction cost for current network
    pub fn estimate_contraction_cost(&self) -> u64 {
        // Simplified cost estimation
        let num_tensors = self.network.tensors.len() as u64;
        let avg_tensor_size = self.network.total_elements() as u64 / num_tensors.max(1);
        num_tensors * avg_tensor_size * avg_tensor_size
    }
}

impl crate::simulator::Simulator for TensorNetworkSimulator {
    fn run<const N: usize>(
        &mut self,
        circuit: &quantrs2_circuit::prelude::Circuit<N>,
    ) -> crate::error::Result<crate::simulator::SimulatorResult<N>> {
        // Initialize zero state
        self.initialize_zero_state().map_err(|e| {
            crate::error::SimulatorError::ComputationError(format!(
                "Failed to initialize state: {}",
                e
            ))
        })?;

        // For now, create a placeholder result with correct dimensions
        let num_states = 1 << N;
        let mut amplitudes = vec![Complex64::new(0.0, 0.0); num_states];

        // Set |00...0⟩ state as default
        if !amplitudes.is_empty() {
            amplitudes[0] = Complex64::new(1.0, 0.0);
        }

        // TODO: Implement proper circuit execution for tensor networks
        Ok(crate::simulator::SimulatorResult::new(amplitudes))
    }
}

impl Default for TensorNetworkSimulator {
    fn default() -> Self {
        Self::new(1)
    }
}

impl fmt::Display for TensorNetwork {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        writeln!(f, "TensorNetwork with {} qubits:", self.num_qubits)?;
        writeln!(f, "  Tensors: {}", self.tensors.len())?;
        writeln!(f, "  Connections: {}", self.connections.len())?;
        writeln!(f, "  Memory usage: {} bytes", self.memory_usage())?;
        Ok(())
    }
}

// Helper functions for common gate matrices
fn pauli_x() -> Array2<Complex64> {
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(0.0, 0.0),
            Complex64::new(1.0, 0.0),
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
        ],
    )
    .unwrap()
}

fn pauli_y() -> Array2<Complex64> {
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, -1.0),
            Complex64::new(0.0, 1.0),
            Complex64::new(0.0, 0.0),
        ],
    )
    .unwrap()
}

fn pauli_z() -> Array2<Complex64> {
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(-1.0, 0.0),
        ],
    )
    .unwrap()
}

fn pauli_h() -> Array2<Complex64> {
    let inv_sqrt2 = 1.0 / 2.0_f64.sqrt();
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(inv_sqrt2, 0.0),
            Complex64::new(inv_sqrt2, 0.0),
            Complex64::new(inv_sqrt2, 0.0),
            Complex64::new(-inv_sqrt2, 0.0),
        ],
    )
    .unwrap()
}

fn cnot_matrix() -> Array2<Complex64> {
    Array2::from_shape_vec(
        (4, 4),
        vec![
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            Complex64::new(1.0, 0.0),
            Complex64::new(0.0, 0.0),
        ],
    )
    .unwrap()
}

fn rotation_x(theta: f64) -> Array2<Complex64> {
    let cos_half = (theta / 2.0).cos();
    let sin_half = (theta / 2.0).sin();
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(cos_half, 0.0),
            Complex64::new(0.0, -sin_half),
            Complex64::new(0.0, -sin_half),
            Complex64::new(cos_half, 0.0),
        ],
    )
    .unwrap()
}

fn rotation_y(theta: f64) -> Array2<Complex64> {
    let cos_half = (theta / 2.0).cos();
    let sin_half = (theta / 2.0).sin();
    Array2::from_shape_vec(
        (2, 2),
        vec![
            Complex64::new(cos_half, 0.0),
            Complex64::new(-sin_half, 0.0),
            Complex64::new(sin_half, 0.0),
            Complex64::new(cos_half, 0.0),
        ],
    )
    .unwrap()
}

fn rotation_z(theta: f64) -> Array2<Complex64> {
    let exp_neg = Complex64::from_polar(1.0, -theta / 2.0);
    let exp_pos = Complex64::from_polar(1.0, theta / 2.0);
    Array2::from_shape_vec(
        (2, 2),
        vec![
            exp_neg,
            Complex64::new(0.0, 0.0),
            Complex64::new(0.0, 0.0),
            exp_pos,
        ],
    )
    .unwrap()
}

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

    #[test]
    fn test_tensor_creation() {
        let data = Array3::zeros((2, 2, 1));
        let indices = vec![
            TensorIndex {
                id: 0,
                dimension: 2,
                index_type: IndexType::Physical(0),
            },
            TensorIndex {
                id: 1,
                dimension: 2,
                index_type: IndexType::Physical(0),
            },
        ];
        let tensor = Tensor::new(data, indices, "test".to_string());

        assert_eq!(tensor.rank(), 2);
        assert_eq!(tensor.label, "test");
    }

    #[test]
    fn test_tensor_network_creation() {
        let network = TensorNetwork::new(3);
        assert_eq!(network.num_qubits, 3);
        assert_eq!(network.tensors.len(), 0);
    }

    #[test]
    fn test_simulator_initialization() {
        let mut sim = TensorNetworkSimulator::new(2);
        sim.initialize_zero_state().unwrap();

        assert_eq!(sim.network.tensors.len(), 2);
    }

    #[test]
    fn test_single_qubit_gate() {
        let mut sim = TensorNetworkSimulator::new(1);
        sim.initialize_zero_state().unwrap();

        let initial_tensors = sim.network.tensors.len();
        let h_gate = QuantumGate::new(
            crate::adaptive_gate_fusion::GateType::Hadamard,
            vec![0],
            vec![],
        );
        sim.apply_gate(h_gate).unwrap();

        // Should add one more tensor for the gate
        assert_eq!(sim.network.tensors.len(), initial_tensors + 1);
    }

    #[test]
    fn test_measurement() {
        let mut sim = TensorNetworkSimulator::new(1);
        sim.initialize_zero_state().unwrap();

        let result = sim.measure(0).unwrap();
        assert!(result == true || result == false); // Just check it returns a bool
    }

    #[test]
    fn test_contraction_strategies() {
        let _sim = TensorNetworkSimulator::new(2);

        // Test different strategies don't crash
        let strat1 = ContractionStrategy::Sequential;
        let strat2 = ContractionStrategy::Greedy;
        let strat3 = ContractionStrategy::Custom(vec![0, 1]);

        assert_ne!(strat1, strat2);
        assert_ne!(strat2, strat3);
    }

    #[test]
    fn test_gate_matrices() {
        let h = pauli_h();
        assert_abs_diff_eq!(h[[0, 0]].re, 1.0 / 2.0_f64.sqrt(), epsilon = 1e-10);

        let x = pauli_x();
        assert_abs_diff_eq!(x[[0, 1]].re, 1.0, epsilon = 1e-10);
        assert_abs_diff_eq!(x[[1, 0]].re, 1.0, epsilon = 1e-10);
    }

    #[test]
    fn test_enhanced_tensor_contraction() {
        let mut id_gen = 0;

        // Create two simple tensors for contraction
        let tensor_a = Tensor::identity(0, &mut id_gen);
        let tensor_b = Tensor::identity(0, &mut id_gen);

        // Contract them
        let result = tensor_a.contract(&tensor_b, 1, 0);
        assert!(result.is_ok());

        let contracted = result.unwrap();
        assert!(contracted.data.len() > 0);
    }

    #[test]
    fn test_contraction_cost_estimation() {
        let network = TensorNetwork::new(2);
        let mut id_gen = 0;

        let tensor_a = Tensor::identity(0, &mut id_gen);
        let tensor_b = Tensor::identity(1, &mut id_gen);

        let cost = network.estimate_contraction_cost(&tensor_a, &tensor_b);
        assert!(cost > 0.0);
        assert!(cost.is_finite());
    }

    #[test]
    fn test_optimal_contraction_order() {
        let mut network = TensorNetwork::new(3);
        let mut id_gen = 0;

        // Add some tensors
        for i in 0..3 {
            let tensor = Tensor::identity(i, &mut id_gen);
            network.add_tensor(tensor);
        }

        let order = network.find_optimal_contraction_order();
        assert!(order.is_ok());

        let order_vec = order.unwrap();
        assert!(!order_vec.is_empty());
    }

    #[test]
    fn test_greedy_contraction_strategy() {
        let mut simulator =
            TensorNetworkSimulator::new(2).with_strategy(ContractionStrategy::Greedy);

        // Add some tensors to the network
        let mut id_gen = 0;
        for i in 0..2 {
            let tensor = Tensor::identity(i, &mut id_gen);
            simulator.network.add_tensor(tensor);
        }

        let result = simulator.contract_greedy();
        assert!(result.is_ok());

        let amplitude = result.unwrap();
        assert!(amplitude.norm() >= 0.0);
    }

    #[test]
    fn test_basis_state_boundary_conditions() {
        let mut network = TensorNetwork::new(2);

        // Add identity tensors
        let mut id_gen = 0;
        for i in 0..2 {
            let tensor = Tensor::identity(i, &mut id_gen);
            network.add_tensor(tensor);
        }

        // Set boundary conditions for |01⟩ state
        let result = network.set_basis_state_boundary(1); // |01⟩ = binary 01
        assert!(result.is_ok());
    }

    #[test]
    fn test_full_state_vector_contraction() {
        let simulator = TensorNetworkSimulator::new(2);

        let result = simulator.contract_network_to_state_vector();
        assert!(result.is_ok());

        let state_vector = result.unwrap();
        assert_eq!(state_vector.len(), 4); // 2^2 = 4 for 2 qubits

        // Should default to |00⟩ state
        assert!((state_vector[0].norm() - 1.0).abs() < 1e-10);
    }

    #[test]
    fn test_advanced_contraction_algorithms() {
        let mut id_gen = 0;
        let tensor = Tensor::identity(0, &mut id_gen);

        // Test HOTQR decomposition
        let qr_result = AdvancedContractionAlgorithms::hotqr_decomposition(&tensor);
        assert!(qr_result.is_ok());

        let (q, r) = qr_result.unwrap();
        assert_eq!(q.label, "Q");
        assert_eq!(r.label, "R");
    }

    #[test]
    fn test_tree_contraction() {
        let mut id_gen = 0;
        let tensors = vec![
            Tensor::identity(0, &mut id_gen),
            Tensor::identity(1, &mut id_gen),
        ];

        let result = AdvancedContractionAlgorithms::tree_contraction(&tensors);
        assert!(result.is_ok());

        let amplitude = result.unwrap();
        assert!(amplitude.norm() >= 0.0);
    }
}

/// Advanced tensor contraction algorithms
pub struct AdvancedContractionAlgorithms;

impl AdvancedContractionAlgorithms {
    /// Implement the HOTQR (Higher Order Tensor QR) decomposition
    pub fn hotqr_decomposition(tensor: &Tensor) -> Result<(Tensor, Tensor)> {
        // Simplified HOTQR - in practice would use specialized tensor libraries
        let mut id_gen = 1000; // Use high IDs to avoid conflicts

        // Create Q and R tensors with appropriate dimensions
        let q_data = Array3::from_shape_fn((2, 2, 1), |(i, j, _)| {
            if i == j {
                Complex64::new(1.0, 0.0)
            } else {
                Complex64::new(0.0, 0.0)
            }
        }); // Simplified Q matrix
        let r_data = Array3::from_shape_fn((2, 2, 1), |(i, j, _)| {
            if i == j {
                Complex64::new(1.0, 0.0)
            } else {
                Complex64::new(0.0, 0.0)
            }
        }); // Simplified R matrix

        let q_indices = vec![
            TensorIndex {
                id: id_gen,
                dimension: 2,
                index_type: IndexType::Virtual,
            },
            TensorIndex {
                id: id_gen + 1,
                dimension: 2,
                index_type: IndexType::Virtual,
            },
        ];
        id_gen += 2;

        let r_indices = vec![
            TensorIndex {
                id: id_gen,
                dimension: 2,
                index_type: IndexType::Virtual,
            },
            TensorIndex {
                id: id_gen + 1,
                dimension: 2,
                index_type: IndexType::Virtual,
            },
        ];

        let q_tensor = Tensor::new(q_data, q_indices, "Q".to_string());
        let r_tensor = Tensor::new(r_data, r_indices, "R".to_string());

        Ok((q_tensor, r_tensor))
    }

    /// Implement Tree Tensor Network contraction
    pub fn tree_contraction(tensors: &[Tensor]) -> Result<Complex64> {
        if tensors.is_empty() {
            return Ok(Complex64::new(1.0, 0.0));
        }

        if tensors.len() == 1 {
            return Ok(tensors[0].data[[0, 0, 0]]);
        }

        // Build binary tree for contraction
        let mut current_level = tensors.to_vec();

        while current_level.len() > 1 {
            let mut next_level = Vec::new();

            // Pair up tensors and contract them
            for chunk in current_level.chunks(2) {
                if chunk.len() == 2 {
                    // Contract the pair
                    let contracted = chunk[0].contract(&chunk[1], 0, 0)?;
                    next_level.push(contracted);
                } else {
                    // Odd tensor out, pass it to next level
                    next_level.push(chunk[0].clone());
                }
            }

            current_level = next_level;
        }

        Ok(current_level[0].data[[0, 0, 0]])
    }

    /// Implement Matrix Product State (MPS) decomposition
    pub fn mps_decomposition(tensor: &Tensor, max_bond_dim: usize) -> Result<Vec<Tensor>> {
        // Simplified MPS decomposition
        let mut mps_tensors = Vec::new();
        let mut id_gen = 2000;

        // For demonstration, create a simple MPS chain
        for i in 0..tensor.indices.len().min(4) {
            let bond_dim = max_bond_dim.min(4);

            let data = Array3::zeros((2, bond_dim, 1));
            // Set some non-zero elements
            let mut mps_data = data;
            mps_data[[0, 0, 0]] = Complex64::new(1.0, 0.0);
            if bond_dim > 1 {
                mps_data[[1, 1, 0]] = Complex64::new(1.0, 0.0);
            }

            let indices = vec![
                TensorIndex {
                    id: id_gen,
                    dimension: 2,
                    index_type: IndexType::Physical(i),
                },
                TensorIndex {
                    id: id_gen + 1,
                    dimension: bond_dim,
                    index_type: IndexType::Virtual,
                },
            ];
            id_gen += 2;

            let mps_tensor = Tensor::new(mps_data, indices, format!("MPS_{}", i));
            mps_tensors.push(mps_tensor);
        }

        Ok(mps_tensors)
    }
}