quantrs2_sim/enhanced_tensor_networks/
functions.rs

1//! Auto-generated module
2//!
3//! 🤖 Generated with [SplitRS](https://github.com/cool-japan/splitrs)
4
5use crate::error::{Result, SimulatorError};
6use crate::scirs2_integration::SciRS2Backend;
7use scirs2_core::ndarray::{Array, Array2, ArrayD, IxDyn};
8use scirs2_core::random::prelude::*;
9use scirs2_core::Complex64;
10
11use super::enhancedtensornetworksimulator_type::EnhancedTensorNetworkSimulator;
12#[cfg(feature = "advanced_math")]
13use super::types::{ContractionIndices, SciRS2Tensor};
14use super::types::{
15    ContractionStep, ContractionStrategy, EnhancedTensor, EnhancedTensorNetworkConfig,
16    EnhancedTensorNetworkUtils, IndexType, TensorIndex, TensorNetwork,
17};
18
19#[cfg(feature = "advanced_math")]
20impl SciRS2Backend {
21    pub(super) fn einsum_contract(
22        &self,
23        _tensor1: &SciRS2Tensor,
24        _tensor2: &SciRS2Tensor,
25        _indices: &ContractionIndices,
26    ) -> Result<SciRS2Tensor> {
27        Ok(SciRS2Tensor {
28            data: ArrayD::zeros(IxDyn(&[2, 2])),
29            shape: vec![2, 2],
30        })
31    }
32}
33#[cfg(test)]
34mod tests {
35    use super::*;
36    use approx::assert_abs_diff_eq;
37    #[test]
38    fn test_enhanced_tensor_network_config() {
39        let config = EnhancedTensorNetworkConfig::default();
40        assert_eq!(config.max_bond_dimension, 1024);
41        assert_eq!(config.contraction_strategy, ContractionStrategy::Adaptive);
42        assert!(config.enable_approximations);
43    }
44    #[test]
45    fn test_tensor_index_creation() {
46        let index = TensorIndex {
47            label: "q0".to_string(),
48            dimension: 2,
49            index_type: IndexType::Physical,
50            connected_tensors: vec![],
51        };
52        assert_eq!(index.label, "q0");
53        assert_eq!(index.dimension, 2);
54        assert_eq!(index.index_type, IndexType::Physical);
55    }
56    #[test]
57    fn test_tensor_network_creation() {
58        let mut network = TensorNetwork::new();
59        assert_eq!(network.tensor_ids().len(), 0);
60        assert_eq!(network.total_size(), 0);
61    }
62    #[test]
63    fn test_enhanced_tensor_creation() {
64        let data = Array::zeros(IxDyn(&[2, 2]));
65        let indices = vec![
66            TensorIndex {
67                label: "i0".to_string(),
68                dimension: 2,
69                index_type: IndexType::Physical,
70                connected_tensors: vec![],
71            },
72            TensorIndex {
73                label: "i1".to_string(),
74                dimension: 2,
75                index_type: IndexType::Physical,
76                connected_tensors: vec![],
77            },
78        ];
79        let tensor = EnhancedTensor {
80            data,
81            indices,
82            bond_dimensions: vec![2, 2],
83            id: 0,
84            memory_size: 4 * std::mem::size_of::<Complex64>(),
85            contraction_cost: 8.0,
86            priority: 1.0,
87        };
88        assert_eq!(tensor.bond_dimensions, vec![2, 2]);
89        assert_abs_diff_eq!(tensor.contraction_cost, 8.0, epsilon = 1e-10);
90    }
91    #[test]
92    fn test_enhanced_tensor_network_simulator() {
93        let config = EnhancedTensorNetworkConfig::default();
94        let mut simulator =
95            EnhancedTensorNetworkSimulator::new(config).expect("simulator creation should succeed");
96        simulator
97            .initialize_state(3)
98            .expect("state initialization should succeed");
99        assert_eq!(simulator.network.tensors.len(), 3);
100    }
101    #[test]
102    fn test_contraction_step() {
103        let step = ContractionStep {
104            tensor_ids: (1, 2),
105            result_id: 3,
106            flops: 1000.0,
107            memory_required: 2048,
108            result_dimensions: vec![2, 2],
109            parallelizable: true,
110        };
111        assert_eq!(step.tensor_ids, (1, 2));
112        assert_eq!(step.result_id, 3);
113        assert_abs_diff_eq!(step.flops, 1000.0, epsilon = 1e-10);
114        assert!(step.parallelizable);
115    }
116    #[test]
117    fn test_memory_estimation() {
118        let memory = EnhancedTensorNetworkUtils::estimate_memory_requirements(10, 20, 64);
119        assert!(memory > 0);
120    }
121    #[test]
122    fn test_contraction_complexity_analysis() {
123        let gate_structure = vec![vec![0], vec![1], vec![0, 1]];
124        let (flops, memory) =
125            EnhancedTensorNetworkUtils::analyze_contraction_complexity(2, &gate_structure);
126        assert!(flops > 0.0);
127        assert!(memory > 0);
128    }
129    #[test]
130    fn test_contraction_strategies() {
131        let strategies = vec![ContractionStrategy::Greedy, ContractionStrategy::Adaptive];
132        let result = EnhancedTensorNetworkUtils::benchmark_contraction_strategies(3, &strategies);
133        assert!(result.is_ok() || result.is_err());
134    }
135    #[test]
136    fn test_enhanced_tensor_network_algorithms() {
137        let config = EnhancedTensorNetworkConfig::default();
138        let simulator =
139            EnhancedTensorNetworkSimulator::new(config).expect("simulator creation should succeed");
140        let tensor_ids = vec![0, 1, 2];
141        let dp_result = simulator.optimize_path_dp(&tensor_ids);
142        assert!(dp_result.is_ok());
143        let tree_result = simulator.optimize_path_tree(&tensor_ids);
144        assert!(tree_result.is_ok());
145        let ml_result = simulator.optimize_path_ml(&tensor_ids);
146        assert!(ml_result.is_ok());
147        let features_result = simulator.extract_network_features(&tensor_ids);
148        assert!(features_result.is_ok());
149        let features = features_result.expect("features extraction should succeed");
150        assert_eq!(features.num_tensors, 3);
151        assert!(features.connectivity_density >= 0.0);
152        let prediction_result = simulator.ml_predict_strategy(&features);
153        assert!(prediction_result.is_ok());
154        let prediction = prediction_result.expect("ML prediction should succeed");
155        assert!(prediction.confidence >= 0.0 && prediction.confidence <= 1.0);
156    }
157}
quantrs2_sim/enhanced_tensor_networks/functions.rs

quantrs2_sim/enhanced_tensor_networks/
functions.rs
