quantrs2-device 0.1.3

//! Mapping algorithm implementations

use super::*;

/// Spectral embedding implementation
pub struct SpectralEmbeddingMapper {
    /// Number of embedding dimensions
    pub embedding_dims: usize,
    /// Normalization method
    pub normalization: SpectralNormalization,
    /// Eigenvalue solver tolerance
    pub tolerance: f64,
}

/// Spectral normalization methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum SpectralNormalization {
    Unnormalized,
    Symmetric,
    RandomWalk,
}

impl SpectralEmbeddingMapper {
    pub fn new(embedding_dims: usize) -> Self {
        Self {
            embedding_dims,
            normalization: SpectralNormalization::Symmetric,
            tolerance: 1e-10,
        }
    }

    pub fn embed_graphs(
        &self,
        logical_graph: &Graph<usize, f64>,
        physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<(Array2<f64>, Array2<f64>)> {
        // Simplified implementation - would need proper Laplacian computation
        let logical_embedding = Array2::zeros((logical_graph.node_count(), self.embedding_dims));
        let physical_embedding = Array2::zeros((physical_graph.node_count(), self.embedding_dims));

        Ok((logical_embedding, physical_embedding))
    }
}

/// Community-based mapping implementation
pub struct CommunityBasedMapper {
    /// Community detection method
    pub method: CommunityMethod,
    /// Resolution parameter
    pub resolution: f64,
    /// Random seed for reproducibility
    pub random_seed: Option<u64>,
}

impl CommunityBasedMapper {
    pub fn new(method: CommunityMethod) -> Self {
        Self {
            method,
            resolution: 1.0,
            random_seed: None,
        }
    }

    pub fn detect_communities(
        &self,
        graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        match self.method {
            CommunityMethod::Louvain => self.louvain_communities_result(graph),
            CommunityMethod::Leiden => self.leiden_communities(graph),
            CommunityMethod::LabelPropagation => self.label_propagation(graph),
            CommunityMethod::SpectralClustering => self.spectral_clustering(graph),
            CommunityMethod::Walktrap => self.walktrap_communities(graph),
        }
    }

    fn louvain_communities_result(
        &self,
        graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Use SciRS2's Louvain implementation
        // louvain_communities_result returns CommunityResult<N> directly
        let community_result = louvain_communities_result(graph);
        // Convert node_communities (HashMap<N, usize>) to our format
        let mut result = HashMap::new();
        for (node, community_id) in community_result.node_communities {
            result.insert(node, community_id);
        }
        Ok(result)
    }

    fn leiden_communities(
        &self,
        _graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Placeholder - would implement Leiden algorithm
        Ok(HashMap::new())
    }

    fn label_propagation(&self, _graph: &Graph<usize, f64>) -> DeviceResult<HashMap<usize, usize>> {
        // Placeholder - would implement label propagation
        Ok(HashMap::new())
    }

    fn spectral_clustering(
        &self,
        _graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Placeholder - would implement spectral clustering
        Ok(HashMap::new())
    }

    fn walktrap_communities(
        &self,
        _graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Placeholder - would implement Walktrap
        Ok(HashMap::new())
    }
}

/// Centrality-weighted mapping implementation
pub struct CentralityWeightedMapper {
    /// Centrality measures to use
    pub centrality_measures: Vec<CentralityMeasure>,
    /// Weights for different centrality measures
    pub centrality_weights: Vec<f64>,
    /// Normalization method
    pub normalization: CentralityNormalization,
}

/// Centrality measure types
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CentralityMeasure {
    Betweenness,
    Closeness,
    Eigenvector,
    PageRank,
    Degree,
}

/// Centrality normalization methods
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CentralityNormalization {
    None,
    MinMax,
    ZScore,
    Softmax,
}

impl CentralityWeightedMapper {
    pub fn new() -> Self {
        Self {
            centrality_measures: vec![
                CentralityMeasure::Betweenness,
                CentralityMeasure::Closeness,
                CentralityMeasure::PageRank,
            ],
            centrality_weights: vec![0.4, 0.3, 0.3],
            normalization: CentralityNormalization::MinMax,
        }
    }

    pub fn calculate_centralities(
        &self,
        graph: &Graph<usize, f64>,
    ) -> DeviceResult<HashMap<usize, f64>> {
        use scirs2_graph::pagerank_centrality;

        let mut combined_centrality = HashMap::new();

        for (measure, weight) in self
            .centrality_measures
            .iter()
            .zip(&self.centrality_weights)
        {
            let centrality = match measure {
                CentralityMeasure::Betweenness => {
                    // betweenness_centrality(graph, normalized) returns HashMap directly
                    betweenness_centrality(graph, true)
                }
                CentralityMeasure::Closeness => {
                    // closeness_centrality(graph, normalized) returns HashMap directly
                    closeness_centrality(graph, true)
                }
                CentralityMeasure::Eigenvector => {
                    // eigenvector_centrality(graph, max_iter, tolerance) returns Result
                    eigenvector_centrality(graph, 100, 1e-6).map_err(|e| {
                        DeviceError::GraphAnalysisError(format!("Eigenvector failed: {:?}", e))
                    })?
                }
                CentralityMeasure::PageRank => {
                    // pagerank_centrality(graph, damping, tolerance) returns Result<HashMap>
                    pagerank_centrality(graph, 0.85, 1e-6).map_err(|e| {
                        DeviceError::GraphAnalysisError(format!("PageRank failed: {:?}", e))
                    })?
                }
                CentralityMeasure::Degree => {
                    // Calculate degree centrality manually
                    // graph.nodes() returns Vec<&N>, use graph.degree(&node)
                    let mut degree_centrality = HashMap::new();
                    for node in graph.nodes() {
                        let degree = graph.degree(node) as f64;
                        degree_centrality.insert(*node, degree);
                    }
                    degree_centrality
                }
            };

            // Normalize centrality values
            let normalized = self.normalize_centrality(&centrality);

            // Combine with weights
            for (node, value) in normalized {
                *combined_centrality.entry(node).or_insert(0.0) += weight * value;
            }
        }

        Ok(combined_centrality)
    }

    fn normalize_centrality(&self, centrality: &HashMap<usize, f64>) -> HashMap<usize, f64> {
        match self.normalization {
            CentralityNormalization::None => centrality.clone(),
            CentralityNormalization::MinMax => {
                let values: Vec<f64> = centrality.values().copied().collect();
                let min_val = values.iter().fold(f64::INFINITY, |a, &b| a.min(b));
                let max_val = values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                let range = max_val - min_val;

                if range > 1e-10 {
                    centrality
                        .iter()
                        .map(|(&k, &v)| (k, (v - min_val) / range))
                        .collect()
                } else {
                    centrality.iter().map(|(&k, _)| (k, 0.5)).collect()
                }
            }
            CentralityNormalization::ZScore => {
                let values: Vec<f64> = centrality.values().copied().collect();
                let mean = values.iter().sum::<f64>() / values.len() as f64;
                let var =
                    values.iter().map(|v| (v - mean).powi(2)).sum::<f64>() / values.len() as f64;
                let std_dev = var.sqrt();

                if std_dev > 1e-10 {
                    centrality
                        .iter()
                        .map(|(&k, &v)| (k, (v - mean) / std_dev))
                        .collect()
                } else {
                    centrality.iter().map(|(&k, _)| (k, 0.0)).collect()
                }
            }
            CentralityNormalization::Softmax => {
                let values: Vec<f64> = centrality.values().copied().collect();
                let max_val = values.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
                let exp_sum: f64 = values.iter().map(|v| (v - max_val).exp()).sum();

                centrality
                    .iter()
                    .map(|(&k, &v)| (k, (v - max_val).exp() / exp_sum))
                    .collect()
            }
        }
    }
}

/// Bipartite matching implementation for optimal assignment
pub struct BipartiteMatchingMapper {
    /// Weight calculation method
    pub weight_method: WeightMethod,
    /// Maximum weight for normalization
    pub max_weight: f64,
}

/// Weight calculation methods for bipartite matching
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum WeightMethod {
    Distance,
    Fidelity,
    Hybrid {
        distance_weight: f64,
        fidelity_weight: f64,
    },
}

impl BipartiteMatchingMapper {
    pub fn new() -> Self {
        Self {
            weight_method: WeightMethod::Hybrid {
                distance_weight: 0.6,
                fidelity_weight: 0.4,
            },
            max_weight: 100.0,
        }
    }

    pub fn find_optimal_mapping(
        &self,
        logical_graph: &Graph<usize, f64>,
        physical_graph: &Graph<usize, f64>,
        calibration: Option<&DeviceCalibration>,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Build bipartite graph for matching
        let (bipartite_graph, coloring, logical_ids, physical_offset) =
            self.build_bipartite_graph(logical_graph, physical_graph, calibration)?;

        // Find maximum weight matching using SciRS2
        // maximum_bipartite_matching returns BipartiteMatching<N> directly (not Result)
        let matching_result = maximum_bipartite_matching(&bipartite_graph, &coloring);

        // Convert matching to mapping
        let mut mapping = HashMap::new();
        for (logical_node, physical_node) in matching_result.matching {
            // Reverse the offset applied to physical nodes
            let logical_id = logical_node;
            let physical_id = physical_node.saturating_sub(physical_offset);
            mapping.insert(logical_id, physical_id);
        }

        // Fill in any missing logical qubits with sequential assignment
        for logical_id in &logical_ids {
            if !mapping.contains_key(logical_id) {
                // Find first unused physical qubit
                let used_physical: std::collections::HashSet<_> = mapping.values().collect();
                for phys in 0..physical_graph.node_count() {
                    if !used_physical.contains(&phys) {
                        mapping.insert(*logical_id, phys);
                        break;
                    }
                }
            }
        }

        Ok(mapping)
    }

    fn build_bipartite_graph(
        &self,
        logical_graph: &Graph<usize, f64>,
        physical_graph: &Graph<usize, f64>,
        calibration: Option<&DeviceCalibration>,
    ) -> DeviceResult<(Graph<usize, f64>, HashMap<usize, u8>, Vec<usize>, usize)> {
        let mut bipartite = Graph::new();
        let mut coloring = HashMap::new();

        // Offset for physical nodes to distinguish from logical nodes
        let physical_offset = 1000;

        // Add logical nodes (left side, color = 0)
        // graph.nodes() returns Vec<&N> where N is the node data type (usize)
        let mut logical_ids = Vec::new();
        for &node_data in logical_graph.nodes() {
            bipartite.add_node(node_data);
            coloring.insert(node_data, 0u8);
            logical_ids.push(node_data);
        }

        // Add physical nodes (right side, color = 1)
        let mut physical_ids = Vec::new();
        for &node_data in physical_graph.nodes() {
            let offset_node = node_data + physical_offset;
            bipartite.add_node(offset_node);
            coloring.insert(offset_node, 1u8);
            physical_ids.push(node_data);
        }

        // Add weighted edges between all logical-physical pairs
        for &logical_id in &logical_ids {
            for &physical_id in &physical_ids {
                let weight = self.calculate_assignment_weight(logical_id, physical_id, calibration);
                // Use Result from add_edge but ignore it (edges are always added)
                let _ = bipartite.add_edge(logical_id, physical_id + physical_offset, weight);
            }
        }

        Ok((bipartite, coloring, logical_ids, physical_offset))
    }

    fn calculate_assignment_weight(
        &self,
        _logical_id: usize,
        _physical_id: usize,
        calibration: Option<&DeviceCalibration>,
    ) -> f64 {
        match self.weight_method {
            WeightMethod::Distance => {
                // Simplified distance calculation
                1.0
            }
            WeightMethod::Fidelity => {
                if let Some(cal) = calibration {
                    cal.single_qubit_fidelity(_physical_id).unwrap_or(0.99)
                } else {
                    0.99
                }
            }
            WeightMethod::Hybrid {
                distance_weight,
                fidelity_weight,
            } => {
                let distance_score = 1.0; // Simplified
                let fidelity_score = if let Some(cal) = calibration {
                    cal.single_qubit_fidelity(_physical_id).unwrap_or(0.99)
                } else {
                    0.99
                };

                distance_weight * distance_score + fidelity_weight * fidelity_score
            }
        }
    }
}

/// Multi-level graph partitioning implementation
pub struct MultilevelPartitioner {
    /// Number of levels for coarsening
    pub num_levels: usize,
    /// Coarsening ratio per level
    pub coarsening_ratio: f64,
    /// Partitioning algorithm for coarsest level
    pub base_partitioner: BasePartitioner,
}

/// Base partitioning algorithms
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum BasePartitioner {
    SpectralBisection,
    KernighanLin,
    FiducciaMattheyses,
    RandomBisection,
}

impl MultilevelPartitioner {
    pub fn new() -> Self {
        Self {
            num_levels: 5,
            coarsening_ratio: 0.5,
            base_partitioner: BasePartitioner::SpectralBisection,
        }
    }

    pub fn partition_graph(
        &self,
        graph: &Graph<usize, f64>,
        num_partitions: usize,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Simplified multilevel partitioning
        // graph.nodes() returns Vec<&N> where N is the node data type (usize)
        let mut partition = HashMap::new();
        let nodes = graph.nodes();

        for (i, node_data) in nodes.iter().enumerate() {
            partition.insert(**node_data, i % num_partitions);
        }

        Ok(partition)
    }
}