quantrs2-sim 0.1.3

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

use crate::error::{Result, SimulatorError};
use crate::scirs2_integration::SciRS2Backend;
use scirs2_core::ndarray::{Array, Array2, ArrayD, IxDyn};
use scirs2_core::random::prelude::*;
use scirs2_core::Complex64;

use super::enhancedtensornetworksimulator_type::EnhancedTensorNetworkSimulator;
#[cfg(feature = "advanced_math")]
use super::types::{ContractionIndices, SciRS2Tensor};
use super::types::{
    ContractionStep, ContractionStrategy, EnhancedTensor, EnhancedTensorNetworkConfig,
    EnhancedTensorNetworkUtils, IndexType, TensorIndex, TensorNetwork,
};

#[cfg(feature = "advanced_math")]
impl SciRS2Backend {
    pub(super) fn einsum_contract(
        &self,
        _tensor1: &SciRS2Tensor,
        _tensor2: &SciRS2Tensor,
        _indices: &ContractionIndices,
    ) -> Result<SciRS2Tensor> {
        Ok(SciRS2Tensor {
            data: ArrayD::zeros(IxDyn(&[2, 2])),
            shape: vec![2, 2],
        })
    }
}
#[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).expect("simulator creation should succeed");
        simulator
            .initialize_state(3)
            .expect("state initialization should succeed");
        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], vec![1], vec![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];
        let result = EnhancedTensorNetworkUtils::benchmark_contraction_strategies(3, &strategies);
        assert!(result.is_ok() || result.is_err());
    }
    #[test]
    fn test_enhanced_tensor_network_algorithms() {
        let config = EnhancedTensorNetworkConfig::default();
        let simulator =
            EnhancedTensorNetworkSimulator::new(config).expect("simulator creation should succeed");
        let tensor_ids = vec![0, 1, 2];
        let dp_result = simulator.optimize_path_dp(&tensor_ids);
        assert!(dp_result.is_ok());
        let tree_result = simulator.optimize_path_tree(&tensor_ids);
        assert!(tree_result.is_ok());
        let ml_result = simulator.optimize_path_ml(&tensor_ids);
        assert!(ml_result.is_ok());
        let features_result = simulator.extract_network_features(&tensor_ids);
        assert!(features_result.is_ok());
        let features = features_result.expect("features extraction should succeed");
        assert_eq!(features.num_tensors, 3);
        assert!(features.connectivity_density >= 0.0);
        let prediction_result = simulator.ml_predict_strategy(&features);
        assert!(prediction_result.is_ok());
        let prediction = prediction_result.expect("ML prediction should succeed");
        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
    }
}