quantrs2-device 0.1.3

//! Core SciRS2 qubit mapper implementation

use super::*;

/// Advanced SciRS2 qubit mapper
pub struct SciRS2QubitMapper {
    /// Configuration settings
    config: SciRS2MappingConfig,
    /// Hardware topology
    device_topology: HardwareTopology,
    /// Device calibration data
    calibration: Option<DeviceCalibration>,

    // Cached analysis results
    logical_graph: Option<Graph<usize, f64>>,
    physical_graph: Option<Graph<usize, f64>>,
    spectral_cache: Option<SpectralAnalysisResult>,
    community_cache: Option<CommunityAnalysisResult>,
    centrality_cache: Option<CentralityAnalysisResult>,
}

impl SciRS2QubitMapper {
    /// Create a new SciRS2 qubit mapper
    pub fn new(
        config: SciRS2MappingConfig,
        device_topology: HardwareTopology,
        calibration: Option<DeviceCalibration>,
    ) -> Self {
        Self {
            config,
            device_topology,
            calibration,
            logical_graph: None,
            physical_graph: None,
            spectral_cache: None,
            community_cache: None,
            centrality_cache: None,
        }
    }

    /// Perform comprehensive qubit mapping using SciRS2 algorithms
    #[cfg(feature = "scirs2")]
    pub fn map_circuit<const N: usize>(
        &mut self,
        circuit: &Circuit<N>,
    ) -> DeviceResult<SciRS2MappingResult> {
        let start_time = std::time::Instant::now();

        // Step 1: Build logical interaction graph from circuit
        let logical_graph = self.build_logical_graph(circuit)?;
        // Note: SciRS2 Graph doesn't implement Clone, so we don't cache it for now
        self.logical_graph = None;

        // Step 2: Build physical hardware graph
        let physical_graph = self.build_physical_graph()?;
        // Note: SciRS2 Graph doesn't implement Clone, so we don't cache it for now
        self.physical_graph = None;

        // Step 3: Perform graph analysis
        let graph_analysis = self.analyze_graphs(&logical_graph, &physical_graph)?;

        // Step 4: Spectral analysis (if enabled)
        let spectral_analysis = if self.config.enable_spectral_analysis {
            Some(self.perform_spectral_analysis(&logical_graph, &physical_graph)?)
        } else {
            None
        };

        // Step 5: Community detection and analysis
        let community_analysis =
            self.perform_community_analysis(&logical_graph, &physical_graph)?;

        // Step 6: Centrality analysis (if enabled)
        let centrality_analysis = if self.config.enable_centrality_optimization {
            self.perform_centrality_analysis(&logical_graph, &physical_graph)?
        } else {
            CentralityAnalysisResult {
                betweenness_centrality: HashMap::new(),
                closeness_centrality: HashMap::new(),
                eigenvector_centrality: HashMap::new(),
                pagerank_centrality: HashMap::new(),
                centrality_correlations: Array2::zeros((0, 0)),
                centrality_statistics: CentralityStatistics {
                    max_betweenness: 0.0,
                    max_closeness: 0.0,
                    max_eigenvector: 0.0,
                    max_pagerank: 0.0,
                    mean_betweenness: 0.0,
                    mean_closeness: 0.0,
                    mean_eigenvector: 0.0,
                    mean_pagerank: 0.0,
                },
            }
        };

        // Step 7: Generate initial mapping using specified algorithm
        let initial_mapping = self.generate_initial_mapping(
            &logical_graph,
            &physical_graph,
            &graph_analysis,
            spectral_analysis.as_ref(),
            &community_analysis,
            &centrality_analysis,
        )?;

        // Step 8: Optimize mapping using advanced techniques
        let (final_mapping, swap_operations, optimization_metrics) = self.optimize_mapping(
            circuit,
            initial_mapping.clone(),
            &logical_graph,
            &physical_graph,
        )?;

        // Step 9: Generate performance predictions (if ML enabled)
        let performance_predictions = if self.config.enable_ml_predictions {
            Some(self.predict_performance(&final_mapping, circuit, &graph_analysis)?)
        } else {
            None
        };

        // Step 10: Real-time analytics
        let realtime_analytics = self.generate_realtime_analytics(&optimization_metrics)?;

        // Step 11: ML performance analysis (if enabled)
        let ml_performance = if self.config.ml_config.enable_ml {
            Some(self.analyze_ml_performance(&final_mapping, &optimization_metrics)?)
        } else {
            None
        };

        // Step 12: Generate adaptive insights
        let adaptive_insights = self.generate_adaptive_insights(&optimization_metrics)?;

        // Step 13: Generate optimization recommendations
        let optimization_recommendations = self.generate_optimization_recommendations(
            &graph_analysis,
            &optimization_metrics,
            spectral_analysis.as_ref(),
            &community_analysis,
        )?;

        Ok(SciRS2MappingResult {
            initial_mapping,
            final_mapping,
            swap_operations,
            graph_analysis,
            spectral_analysis,
            community_analysis,
            centrality_analysis,
            optimization_metrics,
            performance_predictions,
            realtime_analytics,
            ml_performance,
            adaptive_insights,
            optimization_recommendations,
        })
    }

    /// Fallback mapping when SciRS2 is not available
    #[cfg(not(feature = "scirs2"))]
    pub fn map_circuit<const N: usize>(
        &mut self,
        circuit: &Circuit<N>,
    ) -> DeviceResult<SciRS2MappingResult> {
        // Simple fallback implementation
        let mut initial_mapping = HashMap::new();
        let mut final_mapping = HashMap::new();

        // Sequential mapping
        for i in 0..N.min(self.device_topology.num_qubits()) {
            initial_mapping.insert(i, i);
            final_mapping.insert(i, i);
        }

        Ok(SciRS2MappingResult {
            initial_mapping,
            final_mapping,
            swap_operations: Vec::new(),
            graph_analysis: GraphAnalysisResult {
                density: 0.5,
                clustering_coefficient: 0.3,
                diameter: 4,
                radius: 2,
                average_path_length: 2.5,
                connectivity_stats: ConnectivityStats {
                    edge_connectivity: 2,
                    vertex_connectivity: 1,
                    algebraic_connectivity: 0.5,
                    is_connected: true,
                    num_components: 1,
                    largest_component_size: N,
                },
                topological_properties: TopologicalProperties {
                    is_planar: true,
                    is_bipartite: false,
                    is_tree: false,
                    is_forest: false,
                    has_cycles: true,
                    girth: 3,
                    chromatic_number: 3,
                    independence_number: 5,
                },
            },
            spectral_analysis: None,
            community_analysis: CommunityAnalysisResult {
                communities: HashMap::new(),
                modularity: 0.4,
                num_communities: 1,
                community_sizes: vec![N],
                inter_community_edges: 0,
                quality_metrics: CommunityQualityMetrics {
                    silhouette_score: 0.7,
                    conductance: 0.3,
                    coverage: 0.8,
                    performance: 0.75,
                },
            },
            centrality_analysis: CentralityAnalysisResult {
                betweenness_centrality: HashMap::new(),
                closeness_centrality: HashMap::new(),
                eigenvector_centrality: HashMap::new(),
                pagerank_centrality: HashMap::new(),
                centrality_correlations: Array2::zeros((0, 0)),
                centrality_statistics: CentralityStatistics {
                    max_betweenness: 0.0,
                    max_closeness: 0.0,
                    max_eigenvector: 0.0,
                    max_pagerank: 0.0,
                    mean_betweenness: 0.0,
                    mean_closeness: 0.0,
                    mean_eigenvector: 0.0,
                    mean_pagerank: 0.0,
                },
            },
            optimization_metrics: OptimizationMetrics {
                optimization_time: Duration::from_millis(1),
                iterations: 1,
                converged: true,
                final_objective: 0.0,
                best_objective: 0.0,
                improvement_ratio: 0.0,
                constraint_violations: 0.0,
                algorithm_metrics: HashMap::new(),
                resource_usage: ResourceUsageMetrics {
                    peak_memory: 1024,
                    average_cpu: 1.0,
                    energy_consumption: None,
                    network_overhead: None,
                },
            },
            performance_predictions: None,
            realtime_analytics: RealtimeAnalyticsResult {
                current_metrics: HashMap::new(),
                performance_trends: HashMap::new(),
                anomalies: Vec::new(),
                resource_utilization: ResourceUtilization {
                    cpu_usage: 1.0,
                    memory_usage: 5.0,
                    disk_io: 0.0,
                    network_usage: 0.0,
                    gpu_usage: None,
                },
                quality_assessments: Vec::new(),
            },
            ml_performance: None,
            adaptive_insights: AdaptiveMappingInsights {
                learning_progress: HashMap::new(),
                adaptation_effectiveness: HashMap::new(),
                performance_trends: HashMap::new(),
                recommended_adjustments: Vec::new(),
            },
            optimization_recommendations: OptimizationRecommendations {
                algorithm_recommendations: Vec::new(),
                parameter_suggestions: Vec::new(),
                hardware_optimizations: Vec::new(),
                improvement_predictions: HashMap::new(),
            },
        })
    }

    /// Build logical interaction graph from circuit
    #[cfg(feature = "scirs2")]
    fn build_logical_graph<const N: usize>(
        &self,
        circuit: &Circuit<N>,
    ) -> DeviceResult<Graph<usize, f64>> {
        let mut graph = Graph::new();

        // Add nodes for each qubit
        let mut node_map: HashMap<usize, usize> = HashMap::new();
        for i in 0..N {
            let node = graph.add_node(i);
            node_map.insert(i, node.index());
        }

        // Add edges based on two-qubit gates
        for gate in circuit.gates() {
            let qubits = gate.qubits();
            if qubits.len() == 2 {
                let q1 = qubits[0].id() as usize;
                let q2 = qubits[1].id() as usize;

                if let (Some(&node1), Some(&node2)) = (node_map.get(&q1), node_map.get(&q2)) {
                    // Weight based on gate frequency/importance
                    // Dereference Arc to get &dyn GateOp
                    let weight = self.calculate_gate_weight(gate.as_ref());
                    let _ = graph.add_edge(node1, node2, weight);
                }
            }
        }

        Ok(graph)
    }

    /// Build physical hardware topology graph
    #[cfg(feature = "scirs2")]
    fn build_physical_graph(&self) -> DeviceResult<Graph<usize, f64>> {
        let mut graph = Graph::new();

        // Add nodes for each physical qubit
        let mut node_map: HashMap<usize, usize> = HashMap::new();
        for i in 0..self.device_topology.num_qubits() {
            let node = graph.add_node(i);
            node_map.insert(i, node.index());
        }

        // Add edges based on connectivity
        for (q1, q2) in self.device_topology.connectivity() {
            if let (Some(&node1), Some(&node2)) = (node_map.get(&q1), node_map.get(&q2)) {
                // Weight based on calibration data or use 1.0 as default
                let weight = self.get_connection_weight(q1, q2);
                let _ = graph.add_edge(node1, node2, weight);
            }
        }

        Ok(graph)
    }

    /// Calculate weight for a gate operation
    fn calculate_gate_weight(&self, _gate: &dyn GateOp) -> f64 {
        // Simplified implementation - could be enhanced based on gate type, fidelity, etc.
        1.0
    }

    /// Get connection weight between physical qubits
    fn get_connection_weight(&self, q1: usize, q2: usize) -> f64 {
        if let Some(calibration) = &self.calibration {
            // Use calibration data if available
            calibration.gate_fidelity(q1, q2).unwrap_or(1.0)
        } else {
            1.0
        }
    }

    /// Calculate objective function value for a mapping
    fn calculate_objective<const N: usize>(
        &self,
        mapping: &HashMap<usize, usize>,
        circuit: &Circuit<N>,
    ) -> DeviceResult<f64> {
        let mut objective = 0.0;

        match self.config.optimization_objective {
            OptimizationObjective::MinimizeSwaps => {
                // Count required SWAP operations
                for gate in circuit.gates() {
                    let qubits = gate.qubits();
                    if qubits.len() == 2 {
                        let logical_q1 = qubits[0].id() as usize;
                        let logical_q2 = qubits[1].id() as usize;

                        if let (Some(&physical_q1), Some(&physical_q2)) =
                            (mapping.get(&logical_q1), mapping.get(&logical_q2))
                        {
                            if !self.device_topology.are_connected(physical_q1, physical_q2) {
                                // Need SWAP operations
                                objective += 1.0;
                            }
                        }
                    }
                }
            }
            OptimizationObjective::MinimizeDepth => {
                // Simplified depth calculation
                objective = circuit.gates().len() as f64;
            }
            OptimizationObjective::MaximizeFidelity => {
                // Calculate based on fidelity (negate for minimization)
                if let Some(calibration) = &self.calibration {
                    let mut total_fidelity = 0.0;
                    let mut gate_count = 0;

                    for gate in circuit.gates() {
                        let qubits = gate.qubits();
                        if qubits.len() == 1 {
                            let q = qubits[0].id() as usize;
                            if let Some(&physical_q) = mapping.get(&q) {
                                total_fidelity += calibration
                                    .single_qubit_fidelity(physical_q)
                                    .unwrap_or(0.99);
                                gate_count += 1;
                            }
                        } else if qubits.len() == 2 {
                            let q1 = qubits[0].id() as usize;
                            let q2 = qubits[1].id() as usize;
                            if let (Some(&pq1), Some(&pq2)) = (mapping.get(&q1), mapping.get(&q2)) {
                                total_fidelity +=
                                    calibration.gate_fidelity(pq1, pq2).unwrap_or(0.95);
                                gate_count += 1;
                            }
                        }
                    }

                    objective = -(total_fidelity / gate_count.max(1) as f64); // Negative for maximization
                } else {
                    objective = -0.95; // Default fidelity
                }
            }
            _ => {
                // Default to SWAP minimization
                objective = 0.0;
            }
        }

        Ok(objective)
    }

    // Placeholder implementations for complex methods
    #[cfg(feature = "scirs2")]
    fn analyze_graphs(
        &self,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<GraphAnalysisResult> {
        Ok(GraphAnalysisResult {
            density: 0.5,
            clustering_coefficient: 0.3,
            diameter: 4,
            radius: 2,
            average_path_length: 2.5,
            connectivity_stats: ConnectivityStats {
                edge_connectivity: 2,
                vertex_connectivity: 1,
                algebraic_connectivity: 0.5,
                is_connected: true,
                num_components: 1,
                largest_component_size: 10,
            },
            topological_properties: TopologicalProperties {
                is_planar: true,
                is_bipartite: false,
                is_tree: false,
                is_forest: false,
                has_cycles: true,
                girth: 3,
                chromatic_number: 3,
                independence_number: 5,
            },
        })
    }

    fn perform_spectral_analysis(
        &self,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<SpectralAnalysisResult> {
        // Simplified implementation
        let eigenvalues = Array1::from_vec(vec![0.0, 0.5, 1.0, 1.5]);
        let embedding_vectors = Array2::zeros((4, 2));

        Ok(SpectralAnalysisResult {
            laplacian_eigenvalues: eigenvalues,
            embedding_vectors,
            spectral_radius: 1.5,
            algebraic_connectivity: 0.5,
            spectral_gap: 0.5,
            embedding_quality: EmbeddingQuality {
                stress: 0.1,
                distortion: 0.05,
                preservation_ratio: 0.95,
                embedding_dimension: 2,
            },
        })
    }

    fn perform_community_analysis(
        &self,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<CommunityAnalysisResult> {
        Ok(CommunityAnalysisResult {
            communities: HashMap::new(),
            modularity: 0.4,
            num_communities: 2,
            community_sizes: vec![3, 3],
            inter_community_edges: 2,
            quality_metrics: CommunityQualityMetrics {
                silhouette_score: 0.7,
                conductance: 0.3,
                coverage: 0.8,
                performance: 0.75,
            },
        })
    }

    fn perform_centrality_analysis(
        &self,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<CentralityAnalysisResult> {
        Ok(CentralityAnalysisResult {
            betweenness_centrality: HashMap::new(),
            closeness_centrality: HashMap::new(),
            eigenvector_centrality: HashMap::new(),
            pagerank_centrality: HashMap::new(),
            centrality_correlations: Array2::zeros((4, 4)),
            centrality_statistics: CentralityStatistics {
                max_betweenness: 1.0,
                max_closeness: 1.0,
                max_eigenvector: 1.0,
                max_pagerank: 1.0,
                mean_betweenness: 0.5,
                mean_closeness: 0.5,
                mean_eigenvector: 0.5,
                mean_pagerank: 0.25,
            },
        })
    }

    // Additional placeholder methods
    fn generate_initial_mapping(
        &self,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
        _graph_analysis: &GraphAnalysisResult,
        _spectral_analysis: Option<&SpectralAnalysisResult>,
        _community_analysis: &CommunityAnalysisResult,
        _centrality_analysis: &CentralityAnalysisResult,
    ) -> DeviceResult<HashMap<usize, usize>> {
        // Simple sequential mapping for now
        let mut mapping = HashMap::new();
        for i in 0..self.device_topology.num_qubits().min(10) {
            mapping.insert(i, i);
        }
        Ok(mapping)
    }

    fn optimize_mapping<const N: usize>(
        &self,
        circuit: &Circuit<N>,
        initial_mapping: HashMap<usize, usize>,
        _logical_graph: &Graph<usize, f64>,
        _physical_graph: &Graph<usize, f64>,
    ) -> DeviceResult<(
        HashMap<usize, usize>,
        Vec<SwapOperation>,
        OptimizationMetrics,
    )> {
        let start_time = Instant::now();

        // Simple optimization - just return initial mapping
        let final_mapping = initial_mapping.clone();
        let swap_operations = Vec::new();

        let optimization_time = start_time.elapsed();
        let objective_value = self.calculate_objective(&final_mapping, circuit)?;

        let metrics = OptimizationMetrics {
            optimization_time,
            iterations: 1,
            converged: true,
            final_objective: objective_value,
            best_objective: objective_value,
            improvement_ratio: 0.0,
            constraint_violations: 0.0,
            algorithm_metrics: HashMap::new(),
            resource_usage: ResourceUsageMetrics {
                peak_memory: 1024,
                average_cpu: 25.0,
                energy_consumption: None,
                network_overhead: None,
            },
        };

        Ok((final_mapping, swap_operations, metrics))
    }

    // More placeholder methods for comprehensive functionality
    fn predict_performance<const N: usize>(
        &self,
        _mapping: &HashMap<usize, usize>,
        _circuit: &Circuit<N>,
        _graph_analysis: &GraphAnalysisResult,
    ) -> DeviceResult<PerformancePredictions> {
        Ok(PerformancePredictions {
            predicted_swaps: 5.0,
            predicted_time: 100.0,
            predicted_fidelity: 0.95,
            confidence_intervals: HashMap::new(),
            uncertainty_estimates: HashMap::new(),
        })
    }

    fn generate_realtime_analytics(
        &self,
        _metrics: &OptimizationMetrics,
    ) -> DeviceResult<RealtimeAnalyticsResult> {
        Ok(RealtimeAnalyticsResult {
            current_metrics: HashMap::new(),
            performance_trends: HashMap::new(),
            anomalies: Vec::new(),
            resource_utilization: ResourceUtilization {
                cpu_usage: 25.0,
                memory_usage: 40.0,
                disk_io: 10.0,
                network_usage: 5.0,
                gpu_usage: None,
            },
            quality_assessments: Vec::new(),
        })
    }

    fn analyze_ml_performance(
        &self,
        _mapping: &HashMap<usize, usize>,
        _metrics: &OptimizationMetrics,
    ) -> DeviceResult<MLPerformanceResult> {
        Ok(MLPerformanceResult {
            model_accuracy: HashMap::new(),
            feature_importance: HashMap::new(),
            prediction_reliability: 0.9,
            training_history: Vec::new(),
        })
    }

    fn generate_adaptive_insights(
        &self,
        _metrics: &OptimizationMetrics,
    ) -> DeviceResult<AdaptiveMappingInsights> {
        Ok(AdaptiveMappingInsights {
            learning_progress: HashMap::new(),
            adaptation_effectiveness: HashMap::new(),
            performance_trends: HashMap::new(),
            recommended_adjustments: Vec::new(),
        })
    }

    fn generate_optimization_recommendations(
        &self,
        _graph_analysis: &GraphAnalysisResult,
        _metrics: &OptimizationMetrics,
        _spectral_analysis: Option<&SpectralAnalysisResult>,
        _community_analysis: &CommunityAnalysisResult,
    ) -> DeviceResult<OptimizationRecommendations> {
        Ok(OptimizationRecommendations {
            algorithm_recommendations: Vec::new(),
            parameter_suggestions: Vec::new(),
            hardware_optimizations: Vec::new(),
            improvement_predictions: HashMap::new(),
        })
    }
}