quantrs2-device 0.1.3

//! Circuit partitioning implementations for distributed quantum computation

use super::super::types::*;
use std::collections::HashMap;
use std::sync::Arc;
use std::time::Duration;

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

impl CircuitPartitioner {
    pub fn new() -> Self {
        Self {
            partitioning_strategies: vec![
                Box::new(GraphBasedPartitioning::new()),
                Box::new(LoadBalancedPartitioning::new()),
            ],
            optimization_engine: Arc::new(PartitionOptimizer::new()),
        }
    }

    pub fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Use the first strategy for simplicity
        if let Some(strategy) = self.partitioning_strategies.first() {
            strategy.partition_circuit(circuit, nodes, config)
        } else {
            Err(DistributedComputationError::CircuitPartitioning(
                "No partitioning strategies available".to_string(),
            ))
        }
    }
}

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

impl GraphBasedPartitioning {
    pub fn new() -> Self {
        Self {
            min_cut_algorithm: "Kernighan-Lin".to_string(),
            load_balancing_weight: 0.3,
            communication_weight: 0.7,
        }
    }
}

impl PartitioningStrategy for GraphBasedPartitioning {
    fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        _config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Enhanced graph-based partitioning logic
        let mut partitions = Vec::new();

        if nodes.is_empty() {
            return Err(DistributedComputationError::CircuitPartitioning(
                "No nodes available for partitioning".to_string(),
            ));
        }

        // Build dependency graph of gates
        let gate_dependencies = self.build_gate_dependency_graph(&circuit.gates);

        // Use min-cut algorithm to partition gates
        let gate_partitions =
            self.min_cut_partition(&circuit.gates, &gate_dependencies, nodes.len());

        let nodes_vec: Vec<_> = nodes.iter().collect();

        for (partition_idx, gate_indices) in gate_partitions.iter().enumerate() {
            let node_idx = partition_idx % nodes_vec.len();
            let (node_id, node_info) = &nodes_vec[node_idx];

            let partition_gates: Vec<_> = gate_indices
                .iter()
                .map(|&idx| circuit.gates[idx].clone())
                .collect();

            // Calculate qubits involved in this partition
            let mut qubits_used = std::collections::HashSet::new();
            for gate in &partition_gates {
                qubits_used.extend(&gate.target_qubits);
                qubits_used.extend(&gate.control_qubits);
            }

            let qubits_needed = qubits_used.len() as u32;

            // Validate node capacity
            if qubits_needed > node_info.capabilities.max_qubits {
                return Err(DistributedComputationError::ResourceAllocation(format!(
                    "Node {} insufficient capacity: needs {} qubits, has {}",
                    node_id.0, qubits_needed, node_info.capabilities.max_qubits
                )));
            }

            // Calculate communication overhead between partitions
            let communication_cost = self.calculate_inter_partition_communication(
                gate_indices,
                &gate_partitions,
                &circuit.gates,
            );

            let estimated_time =
                self.estimate_partition_execution_time(&partition_gates, node_info);
            let gates_count = partition_gates.len() as u32;
            let memory_mb = self.estimate_memory_usage(&partition_gates);
            let entanglement_pairs_needed = self.count_entangling_operations(&partition_gates);

            let partition = CircuitPartition {
                partition_id: uuid::Uuid::new_v4(),
                node_id: (*node_id).clone(),
                gates: partition_gates.clone(),
                dependencies: self.calculate_partition_dependencies(
                    partition_idx,
                    &gate_partitions,
                    &gate_dependencies,
                ),
                input_qubits: qubits_used
                    .iter()
                    .map(|qubit_id| QubitId {
                        node_id: (*node_id).clone(),
                        local_id: qubit_id.local_id,
                        global_id: uuid::Uuid::new_v4(),
                    })
                    .collect(),
                output_qubits: qubits_used
                    .iter()
                    .map(|qubit_id| QubitId {
                        node_id: (*node_id).clone(),
                        local_id: qubit_id.local_id,
                        global_id: uuid::Uuid::new_v4(),
                    })
                    .collect(),
                classical_inputs: vec![],
                estimated_execution_time: estimated_time,
                resource_requirements: ResourceRequirements {
                    qubits_needed,
                    gates_count,
                    memory_mb,
                    execution_time_estimate: estimated_time,
                    entanglement_pairs_needed,
                    classical_communication_bits: communication_cost,
                },
            };
            partitions.push(partition);
        }

        Ok(partitions)
    }

    fn estimate_execution_time(&self, partition: &CircuitPartition, node: &NodeInfo) -> Duration {
        self.estimate_partition_execution_time(&partition.gates, node)
    }

    fn calculate_communication_overhead(
        &self,
        partitions: &[CircuitPartition],
        _nodes: &HashMap<NodeId, NodeInfo>,
    ) -> f64 {
        // Calculate communication overhead based on inter-partition dependencies
        let mut total_overhead = 0.0;

        for partition in partitions {
            // Communication cost based on entanglement pairs needed
            total_overhead +=
                partition.resource_requirements.entanglement_pairs_needed as f64 * 0.5;

            // Add cost for classical communication
            total_overhead +=
                partition.resource_requirements.classical_communication_bits as f64 * 0.01;
        }

        total_overhead
    }
}

impl GraphBasedPartitioning {
    // Private helper methods for enhanced partitioning
    fn build_gate_dependency_graph(&self, gates: &[QuantumGate]) -> Vec<Vec<usize>> {
        let mut dependencies = vec![Vec::new(); gates.len()];

        for (i, gate) in gates.iter().enumerate() {
            for (j, other_gate) in gates.iter().enumerate().take(i) {
                // Check if gates share qubits (dependency)
                let gate_qubits: std::collections::HashSet<_> = gate
                    .target_qubits
                    .iter()
                    .chain(gate.control_qubits.iter())
                    .collect();
                let other_qubits: std::collections::HashSet<_> = other_gate
                    .target_qubits
                    .iter()
                    .chain(other_gate.control_qubits.iter())
                    .collect();

                if !gate_qubits.is_disjoint(&other_qubits) {
                    dependencies[i].push(j);
                }
            }
        }

        dependencies
    }

    fn min_cut_partition(
        &self,
        gates: &[QuantumGate],
        _dependencies: &[Vec<usize>],
        num_partitions: usize,
    ) -> Vec<Vec<usize>> {
        // Simplified min-cut algorithm using balanced partitioning
        let partition_size = gates.len() / num_partitions;
        let mut partitions = Vec::new();

        for i in 0..num_partitions {
            let start = i * partition_size;
            let end = if i == num_partitions - 1 {
                gates.len()
            } else {
                (i + 1) * partition_size
            };
            let partition: Vec<usize> = (start..end).collect();
            partitions.push(partition);
        }

        partitions
    }

    fn calculate_inter_partition_communication(
        &self,
        partition_indices: &[usize],
        all_partitions: &[Vec<usize>],
        gates: &[QuantumGate],
    ) -> u32 {
        let mut communication_bits = 0;

        for &gate_idx in partition_indices {
            let gate = &gates[gate_idx];

            // Check if this gate needs data from other partitions
            for other_partition in all_partitions {
                if other_partition != partition_indices {
                    for &other_gate_idx in other_partition {
                        if other_gate_idx < gate_idx {
                            let other_gate = &gates[other_gate_idx];

                            // Check for qubit overlap (indicates communication needed)
                            let gate_qubits: std::collections::HashSet<_> = gate
                                .target_qubits
                                .iter()
                                .chain(gate.control_qubits.iter())
                                .collect();
                            let other_qubits: std::collections::HashSet<_> = other_gate
                                .target_qubits
                                .iter()
                                .chain(other_gate.control_qubits.iter())
                                .collect();

                            if !gate_qubits.is_disjoint(&other_qubits) {
                                communication_bits += 1; // One bit of communication per shared qubit
                            }
                        }
                    }
                }
            }
        }

        communication_bits
    }

    const fn calculate_partition_dependencies(
        &self,
        _partition_idx: usize,
        _all_partitions: &[Vec<usize>],
        _gate_dependencies: &[Vec<usize>],
    ) -> Vec<uuid::Uuid> {
        // For now, return empty dependencies as this requires more complex logic
        // In a full implementation, this would map partition dependencies to UUIDs
        vec![]
    }

    fn estimate_partition_execution_time(
        &self,
        gates: &[QuantumGate],
        node_info: &NodeInfo,
    ) -> Duration {
        let base_gate_time = Duration::from_nanos(100_000); // 100 microseconds per gate
        let mut total_time = Duration::ZERO;

        for gate in gates {
            let gate_fidelity = node_info
                .capabilities
                .gate_fidelities
                .get(&gate.gate_type)
                .unwrap_or(&0.95);

            // Higher fidelity gates execute faster (better calibration)
            let adjusted_time =
                Duration::from_nanos((base_gate_time.as_nanos() as f64 / gate_fidelity) as u64);
            total_time += adjusted_time;
        }

        // Add coherence time impact if coherence times are available
        if !node_info.capabilities.coherence_times.is_empty() {
            let avg_coherence = node_info
                .capabilities
                .coherence_times
                .values()
                .map(|t| t.as_nanos())
                .sum::<u128>() as f64
                / node_info.capabilities.coherence_times.len() as f64;

            if total_time.as_nanos() as f64 > avg_coherence * 0.5 {
                // Add penalty for operations close to coherence time
                total_time = Duration::from_nanos((total_time.as_nanos() as f64 * 1.2) as u64);
            }
        }

        total_time
    }

    fn estimate_memory_usage(&self, gates: &[QuantumGate]) -> u32 {
        let max_qubit_id = gates
            .iter()
            .flat_map(|g| g.target_qubits.iter().chain(g.control_qubits.iter()))
            .map(|qubit_id| qubit_id.local_id)
            .max()
            .unwrap_or(0);

        // Memory for state vector: 2^n complex numbers (16 bytes each)
        let state_vector_mb = (1u64 << (max_qubit_id + 1)) * 16 / (1024 * 1024);

        // Add overhead for gate operations and classical storage
        let overhead_mb = gates.len() as u64 / 100; // 1MB per 100 gates

        std::cmp::max(state_vector_mb + overhead_mb, 10) as u32 // Minimum 10MB
    }

    fn count_entangling_operations(&self, gates: &[QuantumGate]) -> u32 {
        gates
            .iter()
            .filter(|g| {
                !g.control_qubits.is_empty()
                    || g.gate_type.contains("CX")
                    || g.gate_type.contains("CNOT")
                    || g.gate_type.contains("CZ")
                    || g.gate_type.contains("Bell")
            })
            .count() as u32
    }
}

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

impl LoadBalancedPartitioning {
    pub fn new() -> Self {
        Self {
            load_threshold: 0.8,
            rebalancing_strategy: "min_max".to_string(),
        }
    }
}

impl PartitioningStrategy for LoadBalancedPartitioning {
    fn partition_circuit(
        &self,
        circuit: &QuantumCircuit,
        nodes: &HashMap<NodeId, NodeInfo>,
        config: &DistributedComputationConfig,
    ) -> Result<Vec<CircuitPartition>> {
        // Similar simplified implementation
        let strategy = GraphBasedPartitioning::new();
        strategy.partition_circuit(circuit, nodes, config)
    }

    fn estimate_execution_time(&self, partition: &CircuitPartition, _node: &NodeInfo) -> Duration {
        Duration::from_millis(partition.gates.len() as u64 * 10)
    }

    fn calculate_communication_overhead(
        &self,
        partitions: &[CircuitPartition],
        _nodes: &HashMap<NodeId, NodeInfo>,
    ) -> f64 {
        partitions.len() as f64 * 0.1
    }
}

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

impl PartitionOptimizer {
    pub fn new() -> Self {
        Self {
            objectives: vec![
                OptimizationObjective::MinimizeLatency { weight: 0.3 },
                OptimizationObjective::MaximizeThroughput { weight: 0.3 },
                OptimizationObjective::MinimizeResourceUsage { weight: 0.4 },
            ],
            solver: "genetic_algorithm".to_string(),
            timeout: Duration::from_secs(30),
        }
    }
}